diff --git a/optimization202/ESUPS_case_study/EMDAT_Data.xlsx b/optimization202/ESUPS_case_study/EMDAT_Data.xlsx
new file mode 100644
index 0000000..c4c7c42
Binary files /dev/null and b/optimization202/ESUPS_case_study/EMDAT_Data.xlsx differ
diff --git a/optimization202/ESUPS_case_study/data/Book3.xlsx b/optimization202/ESUPS_case_study/data/Book3.xlsx
new file mode 100644
index 0000000..d2a5b35
Binary files /dev/null and b/optimization202/ESUPS_case_study/data/Book3.xlsx differ
diff --git a/optimization202/ESUPS_case_study/data/madagascar/abilityToRespond.csv b/optimization202/ESUPS_case_study/data/madagascar/abilityToRespond.csv
new file mode 100644
index 0000000..958db29
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/abilityToRespond.csv
@@ -0,0 +1,216 @@
+gglCountry,item,capacityToRespond
+Afghanistan,DEFAULT,10
+Albania,DEFAULT,10
+Algeria,DEFAULT,10
+American Samoa,DEFAULT,10
+Angola,DEFAULT,10
+Anguilla,DEFAULT,10
+Antigua and Barbuda,DEFAULT,10
+Argentina,DEFAULT,10
+Armenia,DEFAULT,10
+Australia,DEFAULT,2.00E+15
+Austria,DEFAULT,2.00E+15
+Azerbaijan,DEFAULT,10
+Bahrain,DEFAULT,10
+Bangladesh,DEFAULT,10
+Barbados,DEFAULT,10
+Belarus,DEFAULT,10
+Belgium,DEFAULT,2.00E+15
+Belize,DEFAULT,10
+Benin,DEFAULT,10
+Bermuda,DEFAULT,10
+Bhutan,DEFAULT,10
+Bolivia,DEFAULT,10
+Bosnia and Herzegovina,DEFAULT,10
+Botswana,DEFAULT,10
+Brazil,DEFAULT,10
+British Virgin Islands,DEFAULT,10
+Brunei,DEFAULT,10
+Bulgaria,DEFAULT,2.00E+15
+Burkina Faso,DEFAULT,10
+Burundi,DEFAULT,10
+Cambodia,DEFAULT,10
+Cameroon,DEFAULT,10
+Canada,DEFAULT,2.00E+15
+Cape Verde,DEFAULT,10
+Cayman Islands,DEFAULT,10
+Central African Republic,DEFAULT,10
+Chad,DEFAULT,10
+Chile,DEFAULT,10
+China,DEFAULT,10
+Colombia,DEFAULT,10
+Comoros,DEFAULT,10
+Congo,DEFAULT,10
+Cook Islands,DEFAULT,10
+Costa Rica,DEFAULT,10
+Cote d'Ivoire,DEFAULT,10
+Croatia,DEFAULT,2.00E+15
+Cuba,DEFAULT,10
+Curacao,DEFAULT,10
+Czech Republic,DEFAULT,2.00E+15
+Democratic Republic of the Congo,DEFAULT,10
+Denmark,DEFAULT,2.00E+15
+Djibouti,DEFAULT,10
+Dominica,DEFAULT,10
+Dominican Republic,DEFAULT,10
+Ecuador,DEFAULT,10
+Egypt,DEFAULT,10
+El Salvador,DEFAULT,10
+Equatorial Guinea,DEFAULT,10
+Eritrea,DEFAULT,10
+Estonia,DEFAULT,2.00E+15
+Ethiopia,DEFAULT,10
+Fiji,DEFAULT,10
+Finland,DEFAULT,2.00E+15
+France,DEFAULT,2.00E+15
+French Guiana,DEFAULT,10
+French Polynesia,DEFAULT,10
+Gabon,DEFAULT,10
+Georgia,DEFAULT,10
+Germany,DEFAULT,2.00E+15
+Ghana,DEFAULT,10
+Greece,DEFAULT,2.00E+15
+Grenada,DEFAULT,10
+Guadeloupe,DEFAULT,10
+Guam,DEFAULT,10
+Guatemala,DEFAULT,10
+Guinea,DEFAULT,10
+Guinea-Bissau,DEFAULT,10
+Guyana,DEFAULT,10
+Haiti,DEFAULT,10
+Honduras,DEFAULT,10
+Hong Kong,DEFAULT,10
+Hungary,DEFAULT,2.00E+15
+Iceland,DEFAULT,2.00E+15
+India,DEFAULT,10
+Indonesia,DEFAULT,10
+Iran,DEFAULT,10
+Iraq,DEFAULT,10
+Ireland,DEFAULT,2.00E+15
+Israel,DEFAULT,10
+Italy,DEFAULT,2.00E+15
+Jamaica,DEFAULT,10
+Japan,DEFAULT,10
+Jordan,DEFAULT,10
+Kazakhstan,DEFAULT,10
+Kenya,DEFAULT,10
+Kiribati,DEFAULT,10
+Kuwait,DEFAULT,10
+Kyrgyzstan,DEFAULT,10
+Laos,DEFAULT,10
+Latvia,DEFAULT,2.00E+15
+Lebanon,DEFAULT,10
+Lesotho,DEFAULT,10
+Liberia,DEFAULT,10
+Libya,DEFAULT,10
+Lithuania,DEFAULT,2.00E+15
+Luxembourg,DEFAULT,2.00E+15
+Macau,DEFAULT,10
+Macedonia (FYROM),DEFAULT,10
+Madagascar,DEFAULT,10
+Malawi,DEFAULT,10
+Malaysia,DEFAULT,10
+Maldives,DEFAULT,10
+Mali,DEFAULT,10
+Malta,DEFAULT,2.00E+15
+Marshall Islands,DEFAULT,10
+Martinique,DEFAULT,10
+Mauritania,DEFAULT,10
+Mauritius,DEFAULT,10
+Mayotte,DEFAULT,10
+Mexico,DEFAULT,10
+Micronesia,DEFAULT,10
+Moldova,DEFAULT,10
+Mongolia,DEFAULT,10
+Montenegro,DEFAULT,10
+Montserrat,DEFAULT,10
+Morocco,DEFAULT,10
+Mozambique,DEFAULT,10
+Namibia,DEFAULT,10
+Nepal,DEFAULT,10
+New Caledonia,DEFAULT,10
+New Zealand,DEFAULT,2.00E+15
+Nicaragua,DEFAULT,10
+Nicosia,DEFAULT,2.00E+15
+Niger,DEFAULT,10
+Nigeria,DEFAULT,10
+Niue,DEFAULT,10
+North Korea,DEFAULT,10
+Northern Mariana Islands,DEFAULT,10
+Norway,DEFAULT,2.00E+15
+Oman,DEFAULT,10
+Pakistan,DEFAULT,10
+Palau,DEFAULT,10
+Panama,DEFAULT,10
+Papua New Guinea,DEFAULT,10
+Paraguay,DEFAULT,10
+Peru,DEFAULT,10
+Philippines,DEFAULT,10
+Poland,DEFAULT,2.00E+15
+Portugal,DEFAULT,2.00E+15
+Puerto Rico,DEFAULT,10
+Qatar,DEFAULT,10
+Republic of the Union of Myanmar,DEFAULT,10
+Reunion,DEFAULT,10
+Romania,DEFAULT,2.00E+15
+Russia,DEFAULT,10
+Rwanda,DEFAULT,10
+Saint Helena,DEFAULT,10
+Saint Kitts and Nevis,DEFAULT,10
+Saint Lucia,DEFAULT,10
+Saint Vincent and the Grenadines,DEFAULT,10
+Samoa,DEFAULT,10
+Sao Tome and Principe,DEFAULT,10
+Saudi Arabia,DEFAULT,10
+Senegal,DEFAULT,10
+Serbia,DEFAULT,10
+Seychelles,DEFAULT,10
+Sierra Leone,DEFAULT,10
+Singapore,DEFAULT,10
+Slovakia,DEFAULT,2.00E+15
+Slovenia,DEFAULT,2.00E+15
+Solomon Islands,DEFAULT,10
+Somalia,DEFAULT,10
+South Africa,DEFAULT,10
+South Korea,DEFAULT,10
+South Sudan,DEFAULT,10
+Spain,DEFAULT,2.00E+15
+Sri Lanka,DEFAULT,10
+Sudan,DEFAULT,10
+Suriname,DEFAULT,10
+Swaziland,DEFAULT,10
+Sweden,DEFAULT,2.00E+15
+Switzerland,DEFAULT,2.00E+15
+Syria,DEFAULT,10
+Taiwan,DEFAULT,10
+Tajikistan,DEFAULT,10
+Tanzania,DEFAULT,10
+Thailand,DEFAULT,10
+The Bahamas,DEFAULT,10
+The Gambia,DEFAULT,10
+The Netherlands,DEFAULT,2.00E+15
+Timor-Leste,DEFAULT,10
+Togo,DEFAULT,10
+Tokelau,DEFAULT,10
+Tonga,DEFAULT,10
+Trinidad and Tobago,DEFAULT,10
+Tunisia,DEFAULT,10
+Turkey,DEFAULT,10
+Turkmenistan,DEFAULT,10
+Turks and Caicos Islands,DEFAULT,10
+Tuvalu,DEFAULT,10
+U.S. Virgin Islands,DEFAULT,10
+Uganda,DEFAULT,10
+Ukraine,DEFAULT,10
+United Arab Emirates,DEFAULT,10
+United Kingdom,DEFAULT,2.00E+15
+United States,DEFAULT,2.00E+15
+Uruguay,DEFAULT,10
+Uzbekistan,DEFAULT,10
+Vanuatu,DEFAULT,10
+Venezuela,DEFAULT,10
+Vietnam,DEFAULT,10
+Wallis and Futuna,DEFAULT,10
+Yemen,DEFAULT,10
+Zambia,DEFAULT,10
+Zimbabwe,DEFAULT,10
diff --git a/optimization202/ESUPS_case_study/data/madagascar/countryContinents.csv b/optimization202/ESUPS_case_study/data/madagascar/countryContinents.csv
new file mode 100644
index 0000000..43921a4
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/countryContinents.csv
@@ -0,0 +1,216 @@
+gglCountry,Continent
+Central African Republic,Africa
+Sao Tome and Principe,Africa
+Cote d'Ivoire,Africa
+Libya,Africa
+Democratic Republic of the Congo,Africa
+The Gambia,Africa
+Guinea-Bissau,Africa
+Cape Verde,Africa
+Reunion,Africa
+Saint Helena,Africa
+Mayotte,Africa
+Tanzania,Africa
+South Sudan,Africa
+Congo,Africa
+Algeria,Africa
+Angola,Africa
+Benin,Africa
+Botswana,Africa
+Burkina Faso,Africa
+Burundi,Africa
+Cameroon,Africa
+Chad,Africa
+Comoros,Africa
+Djibouti,Africa
+Egypt,Africa
+Equatorial Guinea,Africa
+Eritrea,Africa
+Ethiopia,Africa
+Gabon,Africa
+Ghana,Africa
+Guinea,Africa
+Kenya,Africa
+Lesotho,Africa
+Liberia,Africa
+Madagascar,Africa
+Malawi,Africa
+Mali,Africa
+Mauritania,Africa
+Mauritius,Africa
+Morocco,Africa
+Mozambique,Africa
+Namibia,Africa
+Niger,Africa
+Nigeria,Africa
+Rwanda,Africa
+Senegal,Africa
+Seychelles,Africa
+Sierra Leone,Africa
+Somalia,Africa
+South Africa,Africa
+Sudan,Africa
+Swaziland,Africa
+Togo,Africa
+Tunisia,Africa
+Uganda,Africa
+Zambia,Africa
+Zimbabwe,Africa
+Taiwan,Asia
+Macau,Asia
+North Korea,Asia
+Vietnam,Asia
+Timor-Leste,Asia
+Syria,Asia
+Brunei,Asia
+Iran,Asia
+South Korea,Asia
+Israel,Asia
+Yemen,Asia
+Hong Kong,Asia
+Laos,Asia
+China,Asia
+Russia,Asia
+Afghanistan,Asia
+Bahrain,Asia
+Bangladesh,Asia
+Bhutan,Asia
+Cambodia,Asia
+India,Asia
+Indonesia,Asia
+Iraq,Asia
+Japan,Asia
+Jordan,Asia
+Kazakhstan,Asia
+Kuwait,Asia
+Kyrgyzstan,Asia
+Lebanon,Asia
+Malaysia,Asia
+Maldives,Asia
+Mongolia,Asia
+Republic of the Union of Myanmar,Asia
+Nepal,Asia
+Oman,Asia
+Pakistan,Asia
+Philippines,Asia
+Qatar,Asia
+Saudi Arabia,Asia
+Singapore,Asia
+Sri Lanka,Asia
+Tajikistan,Asia
+Thailand,Asia
+Turkey,Asia
+Turkmenistan,Asia
+United Arab Emirates,Asia
+Uzbekistan,Asia
+Germany,Europe
+Portugal,Europe
+Czech Republic,Europe
+Moldova,Europe
+Macedonia (FYROM),Europe
+Bosnia and Herzegovina,Europe
+Spain,Europe
+United Kingdom,Europe
+Serbia,Europe
+Albania,Europe
+Armenia,Europe
+Austria,Europe
+Azerbaijan,Europe
+Belarus,Europe
+Belgium,Europe
+Bulgaria,Europe
+Croatia,Europe
+Nicosia,Europe
+Denmark,Europe
+Estonia,Europe
+Finland,Europe
+France,Europe
+Georgia,Europe
+Greece,Europe
+Hungary,Europe
+Iceland,Europe
+Ireland,Europe
+Italy,Europe
+Latvia,Europe
+Lithuania,Europe
+Luxembourg,Europe
+Malta,Europe
+Montenegro,Europe
+The Netherlands,Europe
+Norway,Europe
+Poland,Europe
+Romania,Europe
+Slovakia,Europe
+Slovenia,Europe
+Sweden,Europe
+Switzerland,Europe
+Ukraine,Europe
+Saint Vincent and the Grenadines,NorthAmerica
+British Virgin Islands,NorthAmerica
+Dominican Republic,NorthAmerica
+U.S. Virgin Islands,NorthAmerica
+Curacao,NorthAmerica
+Turks and Caicos Islands,NorthAmerica
+Saint Kitts and Nevis,NorthAmerica
+Martinique,NorthAmerica
+Saint Lucia,NorthAmerica
+Guadeloupe,NorthAmerica
+Anguilla,NorthAmerica
+Antigua and Barbuda,NorthAmerica
+The Bahamas,NorthAmerica
+Barbados,NorthAmerica
+Belize,NorthAmerica
+Bermuda,NorthAmerica
+Canada,NorthAmerica
+Cayman Islands,NorthAmerica
+Costa Rica,NorthAmerica
+Cuba,NorthAmerica
+Dominica,NorthAmerica
+El Salvador,NorthAmerica
+Grenada,NorthAmerica
+Guatemala,NorthAmerica
+Haiti,NorthAmerica
+Honduras,NorthAmerica
+Jamaica,NorthAmerica
+Mexico,NorthAmerica
+Montserrat,NorthAmerica
+Nicaragua,NorthAmerica
+Panama,NorthAmerica
+Puerto Rico,NorthAmerica
+Trinidad and Tobago,NorthAmerica
+United States,NorthAmerica
+Wallis and Futuna,Oceania
+Marshall Islands,Oceania
+Cook Islands,Oceania
+Northern Mariana Islands,Oceania
+Tokelau,Oceania
+Solomon Islands,Oceania
+Micronesia,Oceania
+American Samoa,Oceania
+Australia,Oceania
+Fiji,Oceania
+French Polynesia,Oceania
+Guam,Oceania
+Kiribati,Oceania
+New Caledonia,Oceania
+New Zealand,Oceania
+Niue,Oceania
+Palau,Oceania
+Papua New Guinea,Oceania
+Samoa,Oceania
+Tonga,Oceania
+Tuvalu,Oceania
+Vanuatu,Oceania
+Argentina,SouthAmerica
+Bolivia,SouthAmerica
+Brazil,SouthAmerica
+Chile,SouthAmerica
+Colombia,SouthAmerica
+Ecuador,SouthAmerica
+French Guiana,SouthAmerica
+Guyana,SouthAmerica
+Paraguay,SouthAmerica
+Peru,SouthAmerica
+Suriname,SouthAmerica
+Uruguay,SouthAmerica
+Venezuela,SouthAmerica
diff --git a/optimization202/ESUPS_case_study/data/madagascar/depotCoordinates.csv b/optimization202/ESUPS_case_study/data/madagascar/depotCoordinates.csv
new file mode 100644
index 0000000..f308141
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/depotCoordinates.csv
@@ -0,0 +1,28 @@
+depotCity,depotCountry,gglCountryAscii,gglAddressAscii,gglLat,gglLong
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.2064,45.8658
+Toliary I,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48
diff --git a/optimization202/ESUPS_case_study/data/madagascar/depotMapping.csv b/optimization202/ESUPS_case_study/data/madagascar/depotMapping.csv
new file mode 100644
index 0000000..34ed7cf
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/depotMapping.csv
@@ -0,0 +1,28 @@
+gglAddressAsciiMapFrom,gglAddressAsciiMapTo
+"Ambanja, Madagascar","Ambanja, Madagascar"
+"Ambatondrazaka, Madagascar","Ambatondrazaka, Madagascar"
+"Ambositra, Madagascar","Ambositra, Madagascar"
+"Ambovombe, Madagascar","Ambovombe, Madagascar"
+"Ampanihy, Madagascar","Toliara, Madagascar"
+"Antalaha, Madagascar","Sambava, Madagascar"
+"Antananarivo Renivohitra, Madagascar","Antananarivo Renivohitra, Madagascar"
+"Antsohihy, Madagascar","Antsohihy, Madagascar"
+"Benenitra, Madagascar","Toliara, Madagascar"
+"Farafangana, Madagascar","Farafangana, Madagascar"
+"Fenerive Est, Madagascar","Fenerive Est, Madagascar"
+"Maevatanana, Madagascar","Maevatanana, Madagascar"
+"Mahajanga I, Madagascar","Mahajanga I, Madagascar"
+"Maintirano, Madagascar","Maintirano, Madagascar"
+"Manakara, Madagascar","Manakara, Madagascar"
+"Mananara, Madagascar","Fenerive Est, Madagascar"
+"Maroantsetra, Madagascar","Fenerive Est, Madagascar"
+"Miarinarivo, Madagascar","Miarinarivo, Madagascar"
+"Midongy, Madagascar","Farafangana, Madagascar"
+"Morondava, Madagascar","Morondava, Madagascar"
+"Sainte-Marie, Madagascar","Fenerive Est, Madagascar"
+"Sambava, Madagascar","Sambava, Madagascar"
+"Soanierana Ivongo, Madagascar","Fenerive Est, Madagascar"
+"Taolagnaro, Madagascar","Taolagnaro, Madagascar"
+"Toamasina I, Madagascar","Toamasina I, Madagascar"
+"Toliara, Madagascar","Toliara, Madagascar"
+"Vangaindrano, Madagascar","Farafangana, Madagascar"
diff --git a/optimization202/ESUPS_case_study/data/madagascar/depotSelection.csv b/optimization202/ESUPS_case_study/data/madagascar/depotSelection.csv
new file mode 100644
index 0000000..5bc6632
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/depotSelection.csv
@@ -0,0 +1,28 @@
+gglCountryAscii,gglAddressAscii,include
+Madagascar,"Antananarivo Renivohitra, Madagascar",1
+Madagascar,"Toamasina I, Madagascar",1
+Madagascar,"Ambanja, Madagascar",1
+Madagascar,"Ambatondrazaka, Madagascar",1
+Madagascar,"Ambositra, Madagascar",1
+Madagascar,"Ambovombe, Madagascar",1
+Madagascar,"Ampanihy, Madagascar",1
+Madagascar,"Antalaha, Madagascar",1
+Madagascar,"Antsohihy, Madagascar",1
+Madagascar,"Farafangana, Madagascar",1
+Madagascar,"Fenerive Est, Madagascar",1
+Madagascar,"Mahajanga I, Madagascar",1
+Madagascar,"Maintirano, Madagascar",1
+Madagascar,"Manakara, Madagascar",1
+Madagascar,"Maroantsetra, Madagascar",1
+Madagascar,"Miarinarivo, Madagascar",1
+Madagascar,"Midongy, Madagascar",1
+Madagascar,"Morondava, Madagascar",1
+Madagascar,"Sainte-Marie, Madagascar",1
+Madagascar,"Soanierana Ivongo, Madagascar",1
+Madagascar,"Toliara, Madagascar",1
+Madagascar,"Maevatanana, Madagascar",1
+Madagascar,"Mananara, Madagascar",1
+Madagascar,"Sambava, Madagascar",1
+Madagascar,"Taolagnaro, Madagascar",1
+Madagascar,"Benenitra, Madagascar",1
+Madagascar,"Vangaindrano, Madagascar",1
diff --git a/optimization202/ESUPS_case_study/data/madagascar/disasterAffectedData2.csv b/optimization202/ESUPS_case_study/data/madagascar/disasterAffectedData2.csv
new file mode 100644
index 0000000..fd9d143
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/disasterAffectedData2.csv
@@ -0,0 +1,322 @@
+Day,Month,Year,gglCountry,Type,DisasterID,gglAddress,TotAffected
+0,1,2008,Madagascar,Epidemic,2008-0345-MDG,"Ambatondrazaka, Madagascar",75
+0,1,2008,Madagascar,Epidemic,2008-0345-MDG,"Antananarivo, Madagascar",217
+0,1,2008,Madagascar,Epidemic,2008-0345-MDG,"Antsirabe, Madagascar",125
+0,1,2008,Madagascar,Epidemic,2008-0345-MDG,"Miarinarivo, Madagascar",54
+0,1,2008,Madagascar,Epidemic,2008-0345-MDG,"Taolagnaro, Madagascar",49
+0,2,1998,Madagascar,Flood,1998-0086-MDG,"Antananarivo, Madagascar",7798
+0,2,1998,Madagascar,Flood,1998-0086-MDG,"Fianarantsoa, Madagascar",3119
+0,2,1998,Madagascar,Flood,1998-0086-MDG,"Mahajanga, Madagascar",2007
+0,2,1998,Madagascar,Flood,1998-0086-MDG,"Toamasina, Madagascar",3199
+0,2,1998,Madagascar,Flood,1998-0086-MDG,"Toliara, Madagascar",3877
+0,10,2009,Madagascar,Epidemic,2009-0634-MDG,"Manakara, Madagascar",471
+0,10,2009,Madagascar,Epidemic,2009-0634-MDG,"Mananjary, Madagascar",231
+0,12,1999,Madagascar,Epidemic,1999-0710-MDG,"Antananarivo, Madagascar",1527
+0,12,1999,Madagascar,Epidemic,1999-0710-MDG,"Antsiranana, Madagascar",376
+0,12,1999,Madagascar,Epidemic,1999-0710-MDG,"Mahajanga, Madagascar",393
+0,12,1999,Madagascar,Epidemic,1999-0710-MDG,"Toliara, Madagascar",759
+1,1,2020,Madagascar,Flood,2020-0016-MDG,"Ambatondrazaka, Madagascar",13385
+1,1,2020,Madagascar,Flood,2020-0016-MDG,"Antananarivo, Madagascar",38573
+1,1,2020,Madagascar,Flood,2020-0016-MDG,"Antsiranana, Madagascar",9485
+1,1,2020,Madagascar,Flood,2020-0016-MDG,"Antsohihy, Madagascar",15994
+1,1,2020,Madagascar,Flood,2020-0016-MDG,"Maevatanana, Madagascar",4206
+1,1,2020,Madagascar,Flood,2020-0016-MDG,"Mahajanga, Madagascar",9927
+1,1,2020,Madagascar,Flood,2020-0016-MDG,"Maintirano, Madagascar",3303
+1,1,2020,Madagascar,Flood,2020-0016-MDG,"Sambava, Madagascar",11972
+2,1,2002,Madagascar,Storm,2002-0004-MDG,"Morondava, Madagascar",533
+2,1,2002,Madagascar,Storm,2002-0004-MDG,"Toliara, Madagascar",1367
+2,3,2005,Madagascar,Flood,2005-0097-MDG,"Antsiranana, Madagascar",8958
+2,3,2005,Madagascar,Flood,2005-0097-MDG,"Antsohihy, Madagascar",15104
+2,3,2005,Madagascar,Flood,2005-0097-MDG,"Farafangana, Madagascar",10336
+2,3,2005,Madagascar,Flood,2005-0097-MDG,"Mahajanga, Madagascar",9375
+2,3,2005,Madagascar,Flood,2005-0097-MDG,"Toamasina, Madagascar",14945
+2,4,2000,Madagascar,Storm,2000-0178-MDG,"Antsohihy, Madagascar",146730
+2,4,2000,Madagascar,Storm,2000-0178-MDG,"Fenoarivo Atsinanana, Madagascar",112705
+2,4,2000,Madagascar,Storm,2000-0178-MDG,"Sambava, Madagascar",109837
+3,1,2007,Madagascar,Storm,2007-0032-MDG,"Mananjary, Madagascar",7313
+4,2,2006,Madagascar,Storm,2006-0089-MDG,"Toliara, Madagascar",6212
+4,7,2002,Madagascar,Epidemic,2002-0477-MDG,"Antananarivo, Madagascar",7420
+4,7,2002,Madagascar,Epidemic,2002-0477-MDG,"Fianarantsoa, Madagascar",2968
+4,7,2002,Madagascar,Epidemic,2002-0477-MDG,"Mahajanga, Madagascar",1910
+4,7,2002,Madagascar,Epidemic,2002-0477-MDG,"Manakara, Madagascar",2945
+4,7,2002,Madagascar,Epidemic,2002-0477-MDG,"Toamasina, Madagascar",3044
+4,7,2002,Madagascar,Epidemic,2002-0477-MDG,"Toliara, Madagascar",3689
+4,10,2018,Madagascar,Epidemic,2018-0433-MDG,"Antananarivo, Madagascar",98415
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Antananarivo, Madagascar",38589
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Antsohihy, Madagascar",16000
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Farafangana, Madagascar",10950
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Fenoarivo Atsinanana, Madagascar",12290
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Fianarantsoa, Madagascar",15436
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Mahajanga, Madagascar",9931
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Manakara, Madagascar",15314
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Mananjary, Madagascar",7526
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Morondava, Madagascar",7472
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Sambava, Madagascar",11977
+5,1,2018,Madagascar,Storm,2018-0008-MDG,"Toamasina, Madagascar",15832
+5,3,2008,Madagascar,Storm,2008-0111-MDG,"Antsiranana, Madagascar",400
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Ambatondrazaka, Madagascar",45
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Antananarivo, Madagascar",131
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Antsirabe, Madagascar",75
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Antsiranana, Madagascar",32
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Antsohihy, Madagascar",54
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Fenoarivo Atsinanana, Madagascar",72
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Fianarantsoa, Madagascar",52
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Maevatanana, Madagascar",14
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Mahajanga, Madagascar",34
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Manakara, Madagascar",52
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Miarinarivo, Madagascar",33
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Sambava, Madagascar",41
+5,10,2013,Madagascar,Epidemic,2013-0559-MDG,"Tsiroanomandidy, Madagascar",24
+6,4,2009,Madagascar,Storm,2009-0137-MDG,"Ambatondrazaka, Madagascar",9078
+6,4,2009,Madagascar,Storm,2009-0137-MDG,"Antsohihy, Madagascar",10848
+6,4,2009,Madagascar,Storm,2009-0137-MDG,"Farafangana, Madagascar",7424
+6,4,2009,Madagascar,Storm,2009-0137-MDG,"Fenoarivo Atsinanana, Madagascar",8332
+6,4,2009,Madagascar,Storm,2009-0137-MDG,"Manakara, Madagascar",10382
+6,4,2009,Madagascar,Storm,2009-0137-MDG,"Sambava, Madagascar",8120
+6,4,2009,Madagascar,Storm,2009-0137-MDG,"Toamasina, Madagascar",10733
+7,2,2015,Madagascar,Storm,2015-0074-MDG,"Ambovombe-Androy, Madagascar",1808
+7,2,2015,Madagascar,Storm,2015-0074-MDG,"Morondava, Madagascar",1402
+7,2,2015,Madagascar,Storm,2015-0074-MDG,"Taolagnaro, Madagascar",1620
+7,2,2015,Madagascar,Storm,2015-0074-MDG,"Toliara, Madagascar",3600
+7,3,2004,Madagascar,Storm,2004-0103-MDG,"Antsiranana, Madagascar",91767
+7,3,2004,Madagascar,Storm,2004-0103-MDG,"Antsohihy, Madagascar",154733
+7,3,2004,Madagascar,Storm,2004-0103-MDG,"Fenoarivo Atsinanana, Madagascar",118853
+7,3,2004,Madagascar,Storm,2004-0103-MDG,"Mahajanga, Madagascar",96041
+7,3,2004,Madagascar,Storm,2004-0103-MDG,"Morondava, Madagascar",72258
+7,3,2004,Madagascar,Storm,2004-0103-MDG,"Sambava, Madagascar",115828
+7,3,2004,Madagascar,Storm,2004-0103-MDG,"Toamasina, Madagascar",153101
+7,3,2004,Madagascar,Storm,2004-0103-MDG,"Toliara, Madagascar",185558
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Ambatondrazaka, Madagascar",31844
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Antananarivo, Madagascar",91767
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Antsirabe, Madagascar",52612
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Fenoarivo Atsinanana, Madagascar",50378
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Fianarantsoa, Madagascar",36708
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Ihosy, Madagascar",10615
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Manakara, Madagascar",36418
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Mananjary, Madagascar",17898
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Miarinarivo, Madagascar",22775
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Sambava, Madagascar",28483
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Toamasina, Madagascar",37649
+7,3,2017,Madagascar,Storm,2017-0075-MDG,"Tsiroanomandidy, Madagascar",17107
+8,2,2007,Madagascar,Flood,2007-0069-MDG,"Antananarivo, Madagascar",33000
+8,5,2003,Madagascar,Storm,2003-0218-MDG,"Toamasina, Madagascar",162086
+9,4,1984,Madagascar,Storm,1984-0034-MDG,"Antsiranana, Madagascar",48967
+9,4,1984,Madagascar,Storm,1984-0034-MDG,"Mahajanga, Madagascar",51248
+9,5,2002,Madagascar,Storm,2002-0281-MDG,"Antsiranana, Madagascar",100695
+9,5,2002,Madagascar,Storm,2002-0281-MDG,"Fenoarivo Atsinanana, Madagascar",130415
+9,5,2002,Madagascar,Storm,2002-0281-MDG,"Sambava, Madagascar",127095
+9,5,2002,Madagascar,Storm,2002-0281-MDG,"Toamasina, Madagascar",167995
+9,12,2003,Madagascar,Storm,2003-0602-MDG,"Antsiranana, Madagascar",72
+9,12,2003,Madagascar,Storm,2003-0602-MDG,"Sambava, Madagascar",92
+9,12,2019,Madagascar,Storm,2019-0601-MDG,"Antsiranana, Madagascar",6377
+9,12,2019,Madagascar,Storm,2019-0601-MDG,"Mahajanga, Madagascar",6674
+9,12,2019,Madagascar,Storm,2019-0601-MDG,"Maintirano, Madagascar",2220
+9,12,2019,Madagascar,Storm,2019-0601-MDG,"Morondava, Madagascar",5021
+10,1,1996,Madagascar,Storm,1996-0060-MDG,"Fenoarivo Atsinanana, Madagascar",43703
+10,1,1996,Madagascar,Storm,1996-0060-MDG,"Toamasina, Madagascar",56297
+10,3,2010,Madagascar,Storm,2010-0104-MDG,"Ambatondrazaka, Madagascar",54529
+10,3,2010,Madagascar,Storm,2010-0104-MDG,"Farafangana, Madagascar",44590
+10,3,2010,Madagascar,Storm,2010-0104-MDG,"Manakara, Madagascar",62363
+10,3,2010,Madagascar,Storm,2010-0104-MDG,"Mananjary, Madagascar",30649
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Ambatondrazaka, Madagascar",645
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Ambovombe-Androy, Madagascar",464
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Antananarivo, Madagascar",1859
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Antsirabe, Madagascar",1066
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Antsiranana, Madagascar",457
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Antsohihy, Madagascar",771
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Farafangana, Madagascar",528
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Fenoarivo Atsinanana, Madagascar",1021
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Fianarantsoa, Madagascar",744
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Ihosy, Madagascar",215
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Maevatanana, Madagascar",203
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Mahajanga, Madagascar",479
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Maintirano, Madagascar",159
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Manakara, Madagascar",738
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Mananjary, Madagascar",363
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Miarinarivo, Madagascar",461
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Morondava, Madagascar",360
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Sambava, Madagascar",577
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Taolagnaro, Madagascar",416
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Toamasina, Madagascar",763
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Toliara, Madagascar",925
+10,12,1983,Madagascar,Storm,1983-0157-MDG,"Tsiroanomandidy, Madagascar",347
+13,1,1994,Madagascar,Storm,1994-0009-MDG,"Ambatondrazaka, Madagascar",79907
+13,1,1994,Madagascar,Storm,1994-0009-MDG,"Ambovombe-Androy, Madagascar",57495
+13,1,1994,Madagascar,Storm,1994-0009-MDG,"Fenoarivo Atsinanana, Madagascar",106149
+13,1,1994,Madagascar,Storm,1994-0009-MDG,"Morondava, Madagascar",44588
+13,1,1994,Madagascar,Storm,1994-0009-MDG,"Toamasina, Madagascar",94474
+13,1,1994,Madagascar,Storm,1994-0009-MDG,"Toliara, Madagascar",114503
+13,1,1994,Madagascar,Storm,1994-0009-MDG,"Tsiroanomandidy, Madagascar",42927
+13,3,2020,Madagascar,Storm,2020-0103-MDG,"Fenoarivo Atsinanana, Madagascar",1621
+13,3,2020,Madagascar,Storm,2020-0103-MDG,"Sambava, Madagascar",1579
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Ambovombe-Androy, Madagascar",6457
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Antananarivo, Madagascar",25861
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Antsohihy, Madagascar",10723
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Farafangana, Madagascar",7338
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Fenoarivo Atsinanana, Madagascar",8237
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Ihosy, Madagascar",2991
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Mahajanga, Madagascar",6656
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Maintirano, Madagascar",2214
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Manakara, Madagascar",10263
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Mananjary, Madagascar",5044
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Morondava, Madagascar",5008
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Sambava, Madagascar",8027
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Taolagnaro, Madagascar",5785
+14,2,2011,Madagascar,Storm,2011-0057-MDG,"Toamasina, Madagascar",10610
+14,2,2012,Madagascar,Storm,2012-0043-MDG,"Ambatondrazaka, Madagascar",39988
+14,2,2012,Madagascar,Storm,2012-0043-MDG,"Antananarivo, Madagascar",115236
+14,2,2012,Madagascar,Storm,2012-0043-MDG,"Antsohihy, Madagascar",47782
+14,2,2012,Madagascar,Storm,2012-0043-MDG,"Toamasina, Madagascar",47278
+14,3,2018,Madagascar,Storm,2018-0086-MDG,"Ambatondrazaka, Madagascar",7922
+14,3,2018,Madagascar,Storm,2018-0086-MDG,"Antsiranana, Madagascar",5614
+14,3,2018,Madagascar,Storm,2018-0086-MDG,"Antsohihy, Madagascar",9466
+14,3,2018,Madagascar,Storm,2018-0086-MDG,"Fenoarivo Atsinanana, Madagascar",7271
+14,3,2018,Madagascar,Storm,2018-0086-MDG,"Manakara, Madagascar",9060
+14,3,2018,Madagascar,Storm,2018-0086-MDG,"Mananjary, Madagascar",4453
+14,3,2018,Madagascar,Storm,2018-0086-MDG,"Sambava, Madagascar",7086
+15,3,1986,Madagascar,Storm,1986-0042-MDG,"Ambatondrazaka, Madagascar",19824
+15,3,1986,Madagascar,Storm,1986-0042-MDG,"Fenoarivo Atsinanana, Madagascar",18195
+15,3,1986,Madagascar,Storm,1986-0042-MDG,"Fianarantsoa, Madagascar",22852
+15,3,1986,Madagascar,Storm,1986-0042-MDG,"Toamasina, Madagascar",23438
+15,3,2007,Madagascar,Storm,2007-0095-MDG,"Antsiranana, Madagascar",41041
+15,3,2007,Madagascar,Storm,2007-0095-MDG,"Antsohihy, Madagascar",69201
+15,3,2007,Madagascar,Storm,2007-0095-MDG,"Fenoarivo Atsinanana, Madagascar",53154
+15,3,2007,Madagascar,Storm,2007-0095-MDG,"Sambava, Madagascar",51801
+15,3,2019,Madagascar,Storm,2019-0110-MDG,"Maintirano, Madagascar",1100
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Ambatondrazaka, Madagascar",11685
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Antananarivo, Madagascar",33675
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Antsirabe, Madagascar",19306
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Antsiranana, Madagascar",8281
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Antsohihy, Madagascar",13963
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Farafangana, Madagascar",9555
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Fenoarivo Atsinanana, Madagascar",7761
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Fianarantsoa, Madagascar",13470
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Maevatanana, Madagascar",3672
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Mahajanga, Madagascar",8667
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Maintirano, Madagascar",2883
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Manakara, Madagascar",13364
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Mananjary, Madagascar",6568
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Miarinarivo, Madagascar",8358
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Morondava, Madagascar",6520
+16,1,2015,Madagascar,Storm,2015-0016-MDG,"Tsiroanomandidy, Madagascar",6277
+16,2,1991,Madagascar,Storm,1991-0344-MDG,"Mahajanga, Madagascar",85264
+16,2,1991,Madagascar,Storm,1991-0344-MDG,"Toliara, Madagascar",164736
+17,2,2000,Madagascar,Storm,2000-0107-MDG,"Antananarivo, Madagascar",208538
+17,2,2000,Madagascar,Storm,2000-0107-MDG,"Antsirabe, Madagascar",119560
+17,2,2000,Madagascar,Storm,2000-0107-MDG,"Fenoarivo Atsinanana, Madagascar",114483
+17,2,2000,Madagascar,Storm,2000-0107-MDG,"Morondava, Madagascar",40379
+17,2,2000,Madagascar,Storm,2000-0107-MDG,"Sambava, Madagascar",64727
+17,2,2000,Madagascar,Storm,2000-0107-MDG,"Toamasina, Madagascar",85557
+17,2,2000,Madagascar,Storm,2000-0107-MDG,"Toliara, Madagascar",103694
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Ambatondrazaka, Madagascar",40307
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Antananarivo, Madagascar",116155
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Antsohihy, Madagascar",48163
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Farafangana, Madagascar",32960
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Fenoarivo Atsinanana, Madagascar",36994
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Fianarantsoa, Madagascar",46463
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Maevatanana, Madagascar",12667
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Mahajanga, Madagascar",29894
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Manakara, Madagascar",46097
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Mananjary, Madagascar",22655
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Morondava, Madagascar",22491
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Toamasina, Madagascar",47655
+17,2,2008,Madagascar,Storm,2008-0070-MDG,"Tsiroanomandidy, Madagascar",21653
+17,3,2009,Madagascar,Storm,2009-0123-MDG,"Ambovombe-Androy, Madagascar",724
+17,3,2009,Madagascar,Storm,2009-0123-MDG,"Morondava, Madagascar",561
+17,3,2009,Madagascar,Storm,2009-0123-MDG,"Taolagnaro, Madagascar",649
+17,3,2009,Madagascar,Storm,2009-0123-MDG,"Toliara, Madagascar",1442
+18,1,2003,Madagascar,Flood,2003-0037-MDG,"Antananarivo, Madagascar",9126
+18,1,2003,Madagascar,Flood,2003-0037-MDG,"Antsohihy, Madagascar",3784
+18,1,2003,Madagascar,Flood,2003-0037-MDG,"Farafangana, Madagascar",2590
+18,1,2003,Madagascar,Flood,2003-0037-MDG,"Maevatanana, Madagascar",995
+18,1,2003,Madagascar,Flood,2003-0037-MDG,"Mahajanga, Madagascar",2349
+18,1,2003,Madagascar,Flood,2003-0037-MDG,"Maintirano, Madagascar",781
+18,1,2003,Madagascar,Flood,2003-0037-MDG,"Toamasina, Madagascar",3744
+18,2,2021,Madagascar,Flood,2021-0090-MDG,"Ambatondrazaka, Madagascar",299
+18,2,2021,Madagascar,Flood,2021-0090-MDG,"Antananarivo, Madagascar",861
+18,2,2021,Madagascar,Flood,2021-0090-MDG,"Maintirano, Madagascar",74
+18,2,2021,Madagascar,Flood,2021-0090-MDG,"Morondava, Madagascar",167
+19,1,2009,Madagascar,Storm,2009-0047-MDG,"Antsohihy, Madagascar",13801
+19,1,2009,Madagascar,Storm,2009-0047-MDG,"Fenoarivo Atsinanana, Madagascar",18273
+19,1,2009,Madagascar,Storm,2009-0047-MDG,"Morondava, Madagascar",6445
+19,1,2009,Madagascar,Storm,2009-0047-MDG,"Sambava, Madagascar",10331
+19,1,2009,Madagascar,Storm,2009-0047-MDG,"Toamasina, Madagascar",13656
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Ambatondrazaka, Madagascar",203
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Antananarivo, Madagascar",585
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Antsirabe, Madagascar",336
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Fenoarivo Atsinanana, Madagascar",135
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Fianarantsoa, Madagascar",234
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Maevatanana, Madagascar",64
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Mahajanga, Madagascar",151
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Miarinarivo, Madagascar",145
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Sambava, Madagascar",182
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Toamasina, Madagascar",240
+19,8,2017,Madagascar,Epidemic,2017-0411-MDG,"Tsiroanomandidy, Madagascar",109
+20,12,1981,Madagascar,Storm,1981-0110-MDG,"Antsiranana, Madagascar",118000
+22,1,2005,Madagascar,Storm,2005-0048-MDG,"Ambovombe-Androy, Madagascar",2669
+22,1,2005,Madagascar,Storm,2005-0048-MDG,"Toliara, Madagascar",5316
+22,2,2013,Madagascar,Storm,2013-0032-MDG,"Antananarivo, Madagascar",23823
+22,2,2013,Madagascar,Storm,2013-0032-MDG,"Morondava, Madagascar",4613
+22,2,2013,Madagascar,Storm,2013-0032-MDG,"Toliara, Madagascar",11846
+23,1,2021,Madagascar,Storm,2021-0036-MDG,"Fenoarivo Atsinanana, Madagascar",306
+23,1,2021,Madagascar,Storm,2021-0036-MDG,"Sambava, Madagascar",299
+23,1,2021,Madagascar,Storm,2021-0036-MDG,"Toamasina, Madagascar",395
+24,1,1997,Madagascar,Storm,1997-0013-MDG,"Farafangana, Madagascar",250148
+24,1,1997,Madagascar,Storm,1997-0013-MDG,"Manakara, Madagascar",349852
+24,1,2012,Madagascar,Storm,2012-0005-MDG,"Maintirano, Madagascar",33
+24,1,2012,Madagascar,Storm,2012-0005-MDG,"Morondava, Madagascar",75
+24,1,2012,Madagascar,Storm,2012-0005-MDG,"Toliara, Madagascar",192
+24,3,1999,Madagascar,Epidemic,1999-0490-MDG,"Antananarivo, Madagascar",5398
+24,3,1999,Madagascar,Epidemic,1999-0490-MDG,"Antsiranana, Madagascar",1327
+24,3,1999,Madagascar,Epidemic,1999-0490-MDG,"Fianarantsoa, Madagascar",2159
+24,3,1999,Madagascar,Epidemic,1999-0490-MDG,"Mahajanga, Madagascar",1389
+24,3,1999,Madagascar,Epidemic,1999-0490-MDG,"Toamasina, Madagascar",2215
+24,3,1999,Madagascar,Epidemic,1999-0490-MDG,"Toliara, Madagascar",2684
+24,3,2005,Madagascar,Flood,2005-0165-MDG,"Taolagnaro, Madagascar",900
+25,12,2006,Madagascar,Storm,2006-0687-MDG,"Mahajanga, Madagascar",138
+25,12,2006,Madagascar,Storm,2006-0687-MDG,"Sambava, Madagascar",166
+26,1,2004,Madagascar,Storm,2004-0029-MDG,"Antananarivo, Madagascar",17268
+26,1,2004,Madagascar,Storm,2004-0029-MDG,"Fianarantsoa, Madagascar",6907
+26,1,2004,Madagascar,Storm,2004-0029-MDG,"Mahajanga, Madagascar",4444
+26,1,2004,Madagascar,Storm,2004-0029-MDG,"Toamasina, Madagascar",7084
+26,1,2004,Madagascar,Storm,2004-0029-MDG,"Toliara, Madagascar",8586
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Ambatondrazaka, Madagascar",8883
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Antananarivo, Madagascar",25598
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Antsiranana, Madagascar",6295
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Farafangana, Madagascar",7264
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Fenoarivo Atsinanana, Madagascar",8153
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Manakara, Madagascar",10159
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Mananjary, Madagascar",4993
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Sambava, Madagascar",7945
+26,2,2012,Madagascar,Storm,2012-0056-MDG,"Taolagnaro, Madagascar",5726
+27,1,2008,Madagascar,Storm,2008-0043-MDG,"Antananarivo, Madagascar",6413
+27,1,2008,Madagascar,Storm,2008-0043-MDG,"Mahajanga, Madagascar",1651
+27,1,2008,Madagascar,Storm,2008-0043-MDG,"Maintirano, Madagascar",549
+27,2,2015,Madagascar,Flood,2015-0057-MDG,"Antananarivo, Madagascar",24000
+28,1,1982,Madagascar,Storm,1982-0147-MDG,"Morondava, Madagascar",13537
+28,1,1982,Madagascar,Storm,1982-0147-MDG,"Sambava, Madagascar",21700
+28,1,1982,Madagascar,Storm,1982-0147-MDG,"Toliara, Madagascar",34763
+29,1,2003,Madagascar,Storm,2003-0065-MDG,"Farafangana, Madagascar",74
+29,1,2003,Madagascar,Storm,2003-0065-MDG,"Fenoarivo Atsinanana, Madagascar",83
+29,1,2003,Madagascar,Storm,2003-0065-MDG,"Manakara, Madagascar",104
+29,1,2003,Madagascar,Storm,2003-0065-MDG,"Mananjary, Madagascar",51
+29,1,2003,Madagascar,Storm,2003-0065-MDG,"Sambava, Madagascar",81
+29,1,2003,Madagascar,Storm,2003-0065-MDG,"Toamasina, Madagascar",107
+29,3,2014,Madagascar,Storm,2014-0092-MDG,"Antsiranana, Madagascar",384
+29,3,2014,Madagascar,Storm,2014-0092-MDG,"Antsohihy, Madagascar",647
+29,3,2014,Madagascar,Storm,2014-0092-MDG,"Maevatanana, Madagascar",170
+29,3,2014,Madagascar,Storm,2014-0092-MDG,"Mahajanga, Madagascar",402
+29,3,2014,Madagascar,Storm,2014-0092-MDG,"Maintirano, Madagascar",134
+29,12,1986,Madagascar,Flood,1986-0144-MDG,"Antananarivo, Madagascar",28223
+30,1,2013,Madagascar,Storm,2013-0026-MDG,"Farafangana, Madagascar",681
+30,1,2013,Madagascar,Storm,2013-0026-MDG,"Fenoarivo Atsinanana, Madagascar",765
+30,1,2013,Madagascar,Storm,2013-0026-MDG,"Manakara, Madagascar",953
+30,1,2013,Madagascar,Storm,2013-0026-MDG,"Mananjary, Madagascar",468
+30,1,2013,Madagascar,Storm,2013-0026-MDG,"Sambava, Madagascar",745
+30,1,2013,Madagascar,Storm,2013-0026-MDG,"Toamasina, Madagascar",985
+31,12,1989,Madagascar,Storm,1989-0122-MDG,"Fenoarivo Atsinanana, Madagascar",10471
+31,12,1989,Madagascar,Storm,1989-0122-MDG,"Fianarantsoa, Madagascar",13151
+31,12,1989,Madagascar,Storm,1989-0122-MDG,"Ihosy, Madagascar",3803
+31,12,1989,Madagascar,Storm,1989-0122-MDG,"Mahajanga, Madagascar",8461
+31,12,1989,Madagascar,Storm,1989-0122-MDG,"Manakara, Madagascar",13047
+31,12,1989,Madagascar,Storm,1989-0122-MDG,"Mananjary, Madagascar",6412
diff --git a/optimization202/ESUPS_case_study/data/madagascar/disasterCoordinates.csv b/optimization202/ESUPS_case_study/data/madagascar/disasterCoordinates.csv
new file mode 100644
index 0000000..21b4ee6
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/disasterCoordinates.csv
@@ -0,0 +1,24 @@
+emdatCity,emdatCountry,gglCountryAscii,gglAddressAscii,gglLat,gglLong
+Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263
+Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25
+Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375
+Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098
+Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803
+Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99
+Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667
+Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167
+Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023
+Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281
+Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167
+Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05
+Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833
+Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872
+Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217
+Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036
+Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295
+Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175
+Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667
+Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835
+Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333
+Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461
+Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48
diff --git a/optimization202/ESUPS_case_study/data/madagascar/disasterTypeSelection.csv b/optimization202/ESUPS_case_study/data/madagascar/disasterTypeSelection.csv
new file mode 100644
index 0000000..b1e235a
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/disasterTypeSelection.csv
@@ -0,0 +1,22 @@
+disasterType,include
+Complex Disasters,
+Drought,
+Earthquake (seismic activity),1
+Earthquake,1
+Epidemic,1
+Extreme temperature,
+Flood,1
+Industrial Accident,
+Insect Infestation,
+Insect infestation,
+Landslide,1
+Mass movement dry,1
+Mass Movement Dry,1
+Mass Movement Wet,1
+Mass movement wet,1
+Miscellaneous accident,
+Storm,1
+Transport accident,
+Transport Accident,
+Volcano,1
+Wildfire,1
diff --git a/optimization202/ESUPS_case_study/data/madagascar/disasters.csv b/optimization202/ESUPS_case_study/data/madagascar/disasters.csv
new file mode 100644
index 0000000..d979150
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/disasters.csv
@@ -0,0 +1,327 @@
+Day,Month,Year,origDistrictAddress,gglCountry,Type,DisasterID,TotAffected,gglAddress
+1,1,2020,Alaotra-Mangoro,Madagascar,Flood,2020-0016-MDG,13385,"Ambatondrazaka, Madagascar"
+1,1,2020,Analamanga,Madagascar,Flood,2020-0016-MDG,38573,"Antananarivo, Madagascar"
+1,1,2020,Betsiboka,Madagascar,Flood,2020-0016-MDG,4206,"Maevatanana, Madagascar"
+1,1,2020,Boeny,Madagascar,Flood,2020-0016-MDG,9927,"Mahajanga, Madagascar"
+1,1,2020,Diana,Madagascar,Flood,2020-0016-MDG,9485,"Antsiranana, Madagascar"
+1,1,2020,Melaky,Madagascar,Flood,2020-0016-MDG,3303,"Maintirano, Madagascar"
+1,1,2020,Sava,Madagascar,Flood,2020-0016-MDG,11972,"Sambava, Madagascar"
+1,1,2020,Sofia,Madagascar,Flood,2020-0016-MDG,15994,"Antsohihy, Madagascar"
+2,4,2000,Analanjirofo,Madagascar,Storm,2000-0178-MDG,112705,"Fenoarivo Atsinanana, Madagascar"
+2,4,2000,Sava,Madagascar,Storm,2000-0178-MDG,109837,"Sambava, Madagascar"
+2,4,2000,Sofia,Madagascar,Storm,2000-0178-MDG,146730,"Antsohihy, Madagascar"
+2,1,2002,Atsimo-Andrefana,Madagascar,Storm,2002-0004-MDG,1367,"Toliara, Madagascar"
+2,1,2002,Menabe,Madagascar,Storm,2002-0004-MDG,533,"Morondava, Madagascar"
+2,3,2005,Atsimo-Atsinanana,Madagascar,Flood,2005-0097-MDG,10336,"Farafangana, Madagascar"
+2,3,2005,Atsinanana,Madagascar,Flood,2005-0097-MDG,14945,"Toamasina, Madagascar"
+2,3,2005,Boeny,Madagascar,Flood,2005-0097-MDG,9375,"Mahajanga, Madagascar"
+2,3,2005,Diana,Madagascar,Flood,2005-0097-MDG,8958,"Antsiranana, Madagascar"
+2,3,2005,Sofia,Madagascar,Flood,2005-0097-MDG,15104,"Antsohihy, Madagascar"
+3,1,2007,Vatovavy,Madagascar,Storm,2007-0032-MDG,7313,"Mananjary, Madagascar"
+4,7,2002,Analamanga,Madagascar,Epidemic,2002-0477-MDG,7420,"Antananarivo, Madagascar"
+4,7,2002,Atsimo-Andrefana,Madagascar,Epidemic,2002-0477-MDG,3689,"Toliara, Madagascar"
+4,7,2002,Atsinanana,Madagascar,Epidemic,2002-0477-MDG,3044,"Toamasina, Madagascar"
+4,7,2002,Boeny,Madagascar,Epidemic,2002-0477-MDG,1910,"Mahajanga, Madagascar"
+4,7,2002,Haute Matsiatra,Madagascar,Epidemic,2002-0477-MDG,2968,"Fianarantsoa, Madagascar"
+4,7,2002,Vatovavy-Fitovinany,Madagascar,Epidemic,2002-0477-MDG,2945,"Manakara, Madagascar"
+4,10,2018,Analamanga,Madagascar,Epidemic,2018-0433-MDG,98415,"Antananarivo, Madagascar"
+4,2,2006,Atsimo-Andrefana,Madagascar,Storm,2006-0089-MDG,6212,"Toliara, Madagascar"
+5,3,2008,Diana,Madagascar,Storm,2008-0111-MDG,400,"Antsiranana, Madagascar"
+5,1,2018,Analamanga,Madagascar,Storm,2018-0008-MDG,38589,"Antananarivo, Madagascar"
+5,1,2018,Analanjirofo,Madagascar,Storm,2018-0008-MDG,12290,"Fenoarivo Atsinanana, Madagascar"
+5,1,2018,Atsimo-Atsinanana,Madagascar,Storm,2018-0008-MDG,10950,"Farafangana, Madagascar"
+5,1,2018,Atsinanana,Madagascar,Storm,2018-0008-MDG,15832,"Toamasina, Madagascar"
+5,1,2018,Boeny,Madagascar,Storm,2018-0008-MDG,9931,"Mahajanga, Madagascar"
+5,1,2018,Haute Matsiatra,Madagascar,Storm,2018-0008-MDG,15436,"Fianarantsoa, Madagascar"
+5,1,2018,Menabe,Madagascar,Storm,2018-0008-MDG,7472,"Morondava, Madagascar"
+5,1,2018,Sava,Madagascar,Storm,2018-0008-MDG,11977,"Sambava, Madagascar"
+5,1,2018,Sofia,Madagascar,Storm,2018-0008-MDG,16000,"Antsohihy, Madagascar"
+5,1,2018,Vatovavy,Madagascar,Storm,2018-0008-MDG,7526,"Mananjary, Madagascar"
+5,1,2018,Vatovavy-Fitovinany,Madagascar,Storm,2018-0008-MDG,15314,"Manakara, Madagascar"
+5,10,2013,Alaotra-Mangoro,Madagascar,Epidemic,2013-0559-MDG,45,"Ambatondrazaka, Madagascar"
+5,10,2013,Amoron'i Mania,Madagascar,Epidemic,2013-0559-MDG,30,"Fenoarivo Atsinanana, Madagascar"
+5,10,2013,Analamanga,Madagascar,Epidemic,2013-0559-MDG,131,"Antananarivo, Madagascar"
+5,10,2013,Analanjirofo,Madagascar,Epidemic,2013-0559-MDG,42,"Fenoarivo Atsinanana, Madagascar"
+5,10,2013,Betsiboka,Madagascar,Epidemic,2013-0559-MDG,14,"Maevatanana, Madagascar"
+5,10,2013,Boeny,Madagascar,Epidemic,2013-0559-MDG,34,"Mahajanga, Madagascar"
+5,10,2013,Bongolava,Madagascar,Epidemic,2013-0559-MDG,24,"Tsiroanomandidy, Madagascar"
+5,10,2013,Diana,Madagascar,Epidemic,2013-0559-MDG,32,"Antsiranana, Madagascar"
+5,10,2013,Haute Matsiatra,Madagascar,Epidemic,2013-0559-MDG,52,"Fianarantsoa, Madagascar"
+5,10,2013,Itasy,Madagascar,Epidemic,2013-0559-MDG,33,"Miarinarivo, Madagascar"
+5,10,2013,Sava,Madagascar,Epidemic,2013-0559-MDG,41,"Sambava, Madagascar"
+5,10,2013,Sofia,Madagascar,Epidemic,2013-0559-MDG,54,"Antsohihy, Madagascar"
+5,10,2013,Vakinankaratra,Madagascar,Epidemic,2013-0559-MDG,75,"Antsirabe, Madagascar"
+5,10,2013,Vatovavy-Fitovinany,Madagascar,Epidemic,2013-0559-MDG,52,"Manakara, Madagascar"
+6,4,2009,Alaotra-Mangoro,Madagascar,Storm,2009-0137-MDG,9078,"Ambatondrazaka, Madagascar"
+6,4,2009,Analanjirofo,Madagascar,Storm,2009-0137-MDG,8332,"Fenoarivo Atsinanana, Madagascar"
+6,4,2009,Atsimo-Atsinanana,Madagascar,Storm,2009-0137-MDG,7424,"Farafangana, Madagascar"
+6,4,2009,Atsinanana,Madagascar,Storm,2009-0137-MDG,10733,"Toamasina, Madagascar"
+6,4,2009,Sava,Madagascar,Storm,2009-0137-MDG,8120,"Sambava, Madagascar"
+6,4,2009,Sofia,Madagascar,Storm,2009-0137-MDG,10848,"Antsohihy, Madagascar"
+6,4,2009,Vatovavy-Fitovinany,Madagascar,Storm,2009-0137-MDG,10382,"Manakara, Madagascar"
+7,3,2004,Analanjirofo,Madagascar,Storm,2004-0103-MDG,118853,"Fenoarivo Atsinanana, Madagascar"
+7,3,2004,Atsimo-Andrefana,Madagascar,Storm,2004-0103-MDG,185558,"Toliara, Madagascar"
+7,3,2004,Atsinanana,Madagascar,Storm,2004-0103-MDG,153101,"Toamasina, Madagascar"
+7,3,2004,Boeny,Madagascar,Storm,2004-0103-MDG,96041,"Mahajanga, Madagascar"
+7,3,2004,Diana,Madagascar,Storm,2004-0103-MDG,91767,"Antsiranana, Madagascar"
+7,3,2004,Menabe,Madagascar,Storm,2004-0103-MDG,72258,"Morondava, Madagascar"
+7,3,2004,Sava,Madagascar,Storm,2004-0103-MDG,115828,"Sambava, Madagascar"
+7,3,2004,Sofia,Madagascar,Storm,2004-0103-MDG,154733,"Antsohihy, Madagascar"
+7,2,2015,Androy,Madagascar,Storm,2015-0074-MDG,1808,"Ambovombe-Androy, Madagascar"
+7,2,2015,Anosy,Madagascar,Storm,2015-0074-MDG,1620,"Taolagnaro, Madagascar"
+7,2,2015,Atsimo-Andrefana,Madagascar,Storm,2015-0074-MDG,3600,"Toliara, Madagascar"
+7,2,2015,Menabe,Madagascar,Storm,2015-0074-MDG,1402,"Morondava, Madagascar"
+7,3,2017,Alaotra-Mangoro,Madagascar,Storm,2017-0075-MDG,31844,"Ambatondrazaka, Madagascar"
+7,3,2017,Amoron'i Mania,Madagascar,Storm,2017-0075-MDG,21151,"Fenoarivo Atsinanana, Madagascar"
+7,3,2017,Analamanga,Madagascar,Storm,2017-0075-MDG,91767,"Antananarivo, Madagascar"
+7,3,2017,Analanjirofo,Madagascar,Storm,2017-0075-MDG,29227,"Fenoarivo Atsinanana, Madagascar"
+7,3,2017,Atsinanana,Madagascar,Storm,2017-0075-MDG,37649,"Toamasina, Madagascar"
+7,3,2017,Bongolava,Madagascar,Storm,2017-0075-MDG,17107,"Tsiroanomandidy, Madagascar"
+7,3,2017,Haute Matsiatra,Madagascar,Storm,2017-0075-MDG,36708,"Fianarantsoa, Madagascar"
+7,3,2017,Ihorombe,Madagascar,Storm,2017-0075-MDG,10615,"Ihosy, Madagascar"
+7,3,2017,Itasy,Madagascar,Storm,2017-0075-MDG,22775,"Miarinarivo, Madagascar"
+7,3,2017,Sava,Madagascar,Storm,2017-0075-MDG,28483,"Sambava, Madagascar"
+7,3,2017,Vakinankaratra,Madagascar,Storm,2017-0075-MDG,52612,"Antsirabe, Madagascar"
+7,3,2017,Vatovavy,Madagascar,Storm,2017-0075-MDG,17898,"Mananjary, Madagascar"
+7,3,2017,Vatovavy-Fitovinany,Madagascar,Storm,2017-0075-MDG,36418,"Manakara, Madagascar"
+8,5,2003,Atsinanana,Madagascar,Storm,2003-0218-MDG,162086,"Toamasina, Madagascar"
+8,2,2007,Analamanga,Madagascar,Flood,2007-0069-MDG,33000,"Antananarivo, Madagascar"
+9,4,1984,Boeny,Madagascar,Storm,1984-0034-MDG,51248,"Mahajanga, Madagascar"
+9,4,1984,Diana,Madagascar,Storm,1984-0034-MDG,48967,"Antsiranana, Madagascar"
+9,5,2002,Analanjirofo,Madagascar,Storm,2002-0281-MDG,130415,"Fenoarivo Atsinanana, Madagascar"
+9,5,2002,Atsinanana,Madagascar,Storm,2002-0281-MDG,167995,"Toamasina, Madagascar"
+9,5,2002,Diana,Madagascar,Storm,2002-0281-MDG,100695,"Antsiranana, Madagascar"
+9,5,2002,Sava,Madagascar,Storm,2002-0281-MDG,127095,"Sambava, Madagascar"
+9,12,2003,Diana,Madagascar,Storm,2003-0602-MDG,72,"Antsiranana, Madagascar"
+9,12,2003,Sava,Madagascar,Storm,2003-0602-MDG,92,"Sambava, Madagascar"
+9,12,2019,Boeny,Madagascar,Storm,2019-0601-MDG,6674,"Mahajanga, Madagascar"
+9,12,2019,Diana,Madagascar,Storm,2019-0601-MDG,6377,"Antsiranana, Madagascar"
+9,12,2019,Melaky,Madagascar,Storm,2019-0601-MDG,2220,"Maintirano, Madagascar"
+9,12,2019,Menabe,Madagascar,Storm,2019-0601-MDG,5021,"Morondava, Madagascar"
+10,12,1983,Alaotra-Mangoro,Madagascar,Storm,1983-0157-MDG,645,"Ambatondrazaka, Madagascar"
+10,12,1983,Amoron'i Mania,Madagascar,Storm,1983-0157-MDG,429,"Fenoarivo Atsinanana, Madagascar"
+10,12,1983,Analamanga,Madagascar,Storm,1983-0157-MDG,1859,"Antananarivo, Madagascar"
+10,12,1983,Analanjirofo,Madagascar,Storm,1983-0157-MDG,592,"Fenoarivo Atsinanana, Madagascar"
+10,12,1983,Androy,Madagascar,Storm,1983-0157-MDG,464,"Ambovombe-Androy, Madagascar"
+10,12,1983,Anosy,Madagascar,Storm,1983-0157-MDG,416,"Taolagnaro, Madagascar"
+10,12,1983,Atsimo-Andrefana,Madagascar,Storm,1983-0157-MDG,925,"Toliara, Madagascar"
+10,12,1983,Atsimo-Atsinanana,Madagascar,Storm,1983-0157-MDG,528,"Farafangana, Madagascar"
+10,12,1983,Atsinanana,Madagascar,Storm,1983-0157-MDG,763,"Toamasina, Madagascar"
+10,12,1983,Betsiboka,Madagascar,Storm,1983-0157-MDG,203,"Maevatanana, Madagascar"
+10,12,1983,Boeny,Madagascar,Storm,1983-0157-MDG,479,"Mahajanga, Madagascar"
+10,12,1983,Bongolava,Madagascar,Storm,1983-0157-MDG,347,"Tsiroanomandidy, Madagascar"
+10,12,1983,Diana,Madagascar,Storm,1983-0157-MDG,457,"Antsiranana, Madagascar"
+10,12,1983,Haute Matsiatra,Madagascar,Storm,1983-0157-MDG,744,"Fianarantsoa, Madagascar"
+10,12,1983,Ihorombe,Madagascar,Storm,1983-0157-MDG,215,"Ihosy, Madagascar"
+10,12,1983,Itasy,Madagascar,Storm,1983-0157-MDG,461,"Miarinarivo, Madagascar"
+10,12,1983,Melaky,Madagascar,Storm,1983-0157-MDG,159,"Maintirano, Madagascar"
+10,12,1983,Menabe,Madagascar,Storm,1983-0157-MDG,360,"Morondava, Madagascar"
+10,12,1983,Sava,Madagascar,Storm,1983-0157-MDG,577,"Sambava, Madagascar"
+10,12,1983,Sofia,Madagascar,Storm,1983-0157-MDG,771,"Antsohihy, Madagascar"
+10,12,1983,Vakinankaratra,Madagascar,Storm,1983-0157-MDG,1066,"Antsirabe, Madagascar"
+10,12,1983,Vatovavy,Madagascar,Storm,1983-0157-MDG,363,"Mananjary, Madagascar"
+10,12,1983,Vatovavy-Fitovinany,Madagascar,Storm,1983-0157-MDG,738,"Manakara, Madagascar"
+10,1,1996,Analanjirofo,Madagascar,Storm,1996-0060-MDG,43703,"Fenoarivo Atsinanana, Madagascar"
+10,1,1996,Atsinanana,Madagascar,Storm,1996-0060-MDG,56297,"Toamasina, Madagascar"
+10,3,2010,Alaotra-Mangoro,Madagascar,Storm,2010-0104-MDG,54529,"Ambatondrazaka, Madagascar"
+10,3,2010,Atsimo-Atsinanana,Madagascar,Storm,2010-0104-MDG,44590,"Farafangana, Madagascar"
+10,3,2010,Vatovavy,Madagascar,Storm,2010-0104-MDG,30649,"Mananjary, Madagascar"
+10,3,2010,Vatovavy-Fitovinany,Madagascar,Storm,2010-0104-MDG,62363,"Manakara, Madagascar"
+13,1,1994,Alaotra-Mangoro,Madagascar,Storm,1994-0009-MDG,79907,"Ambatondrazaka, Madagascar"
+13,1,1994,Amoron'i Mania,Madagascar,Storm,1994-0009-MDG,106149,"Fenoarivo Atsinanana, Madagascar"
+13,1,1994,Androy,Madagascar,Storm,1994-0009-MDG,57495,"Ambovombe-Androy, Madagascar"
+13,1,1994,Atsimo-Andrefana,Madagascar,Storm,1994-0009-MDG,114503,"Toliara, Madagascar"
+13,1,1994,Atsinanana,Madagascar,Storm,1994-0009-MDG,94474,"Toamasina, Madagascar"
+13,1,1994,Bongolava,Madagascar,Storm,1994-0009-MDG,42927,"Tsiroanomandidy, Madagascar"
+13,1,1994,Menabe,Madagascar,Storm,1994-0009-MDG,44588,"Morondava, Madagascar"
+13,3,2020,Analanjirofo,Madagascar,Storm,2020-0103-MDG,1621,"Fenoarivo Atsinanana, Madagascar"
+13,3,2020,Sava,Madagascar,Storm,2020-0103-MDG,1579,"Sambava, Madagascar"
+14,2,2011,Analamanga,Madagascar,Storm,2011-0057-MDG,25861,"Antananarivo, Madagascar"
+14,2,2011,Analanjirofo,Madagascar,Storm,2011-0057-MDG,8237,"Fenoarivo Atsinanana, Madagascar"
+14,2,2011,Androy,Madagascar,Storm,2011-0057-MDG,6457,"Ambovombe-Androy, Madagascar"
+14,2,2011,Anosy,Madagascar,Storm,2011-0057-MDG,5785,"Taolagnaro, Madagascar"
+14,2,2011,Atsimo-Atsinanana,Madagascar,Storm,2011-0057-MDG,7338,"Farafangana, Madagascar"
+14,2,2011,Atsinanana,Madagascar,Storm,2011-0057-MDG,10610,"Toamasina, Madagascar"
+14,2,2011,Boeny,Madagascar,Storm,2011-0057-MDG,6656,"Mahajanga, Madagascar"
+14,2,2011,Ihorombe,Madagascar,Storm,2011-0057-MDG,2991,"Ihosy, Madagascar"
+14,2,2011,Melaky,Madagascar,Storm,2011-0057-MDG,2214,"Maintirano, Madagascar"
+14,2,2011,Menabe,Madagascar,Storm,2011-0057-MDG,5008,"Morondava, Madagascar"
+14,2,2011,Sava,Madagascar,Storm,2011-0057-MDG,8027,"Sambava, Madagascar"
+14,2,2011,Sofia,Madagascar,Storm,2011-0057-MDG,10723,"Antsohihy, Madagascar"
+14,2,2011,Vatovavy,Madagascar,Storm,2011-0057-MDG,5044,"Mananjary, Madagascar"
+14,2,2011,Vatovavy-Fitovinany,Madagascar,Storm,2011-0057-MDG,10263,"Manakara, Madagascar"
+14,2,2012,Alaotra-Mangoro,Madagascar,Storm,2012-0043-MDG,39988,"Ambatondrazaka, Madagascar"
+14,2,2012,Analamanga,Madagascar,Storm,2012-0043-MDG,115236,"Antananarivo, Madagascar"
+14,2,2012,Atsinanana,Madagascar,Storm,2012-0043-MDG,47278,"Toamasina, Madagascar"
+14,2,2012,Sofia,Madagascar,Storm,2012-0043-MDG,47782,"Antsohihy, Madagascar"
+14,3,2018,Alaotra-Mangoro,Madagascar,Storm,2018-0086-MDG,7922,"Ambatondrazaka, Madagascar"
+14,3,2018,Analanjirofo,Madagascar,Storm,2018-0086-MDG,7271,"Fenoarivo Atsinanana, Madagascar"
+14,3,2018,Diana,Madagascar,Storm,2018-0086-MDG,5614,"Antsiranana, Madagascar"
+14,3,2018,Sava,Madagascar,Storm,2018-0086-MDG,7086,"Sambava, Madagascar"
+14,3,2018,Sofia,Madagascar,Storm,2018-0086-MDG,9466,"Antsohihy, Madagascar"
+14,3,2018,Vatovavy,Madagascar,Storm,2018-0086-MDG,4453,"Mananjary, Madagascar"
+14,3,2018,Vatovavy-Fitovinany,Madagascar,Storm,2018-0086-MDG,9060,"Manakara, Madagascar"
+15,3,1986,Alaotra-Mangoro,Madagascar,Storm,1986-0042-MDG,19824,"Ambatondrazaka, Madagascar"
+15,3,1986,Analanjirofo,Madagascar,Storm,1986-0042-MDG,18195,"Fenoarivo Atsinanana, Madagascar"
+15,3,1986,Atsinanana,Madagascar,Storm,1986-0042-MDG,23438,"Toamasina, Madagascar"
+15,3,1986,Haute Matsiatra,Madagascar,Storm,1986-0042-MDG,22852,"Fianarantsoa, Madagascar"
+15,3,2007,Analanjirofo,Madagascar,Storm,2007-0095-MDG,53154,"Fenoarivo Atsinanana, Madagascar"
+15,3,2007,Diana,Madagascar,Storm,2007-0095-MDG,41041,"Antsiranana, Madagascar"
+15,3,2007,Sava,Madagascar,Storm,2007-0095-MDG,51801,"Sambava, Madagascar"
+15,3,2007,Sofia,Madagascar,Storm,2007-0095-MDG,69201,"Antsohihy, Madagascar"
+15,3,2019,Melaky,Madagascar,Storm,2019-0110-MDG,1100,"Maintirano, Madagascar"
+16,2,1991,Atsimo-Andrefana,Madagascar,Storm,1991-0344-MDG,164736,"Toliara, Madagascar"
+16,2,1991,Boeny,Madagascar,Storm,1991-0344-MDG,85264,"Mahajanga, Madagascar"
+16,1,2015,Alaotra-Mangoro,Madagascar,Storm,2015-0016-MDG,11685,"Ambatondrazaka, Madagascar"
+16,1,2015,Amoron'i Mania,Madagascar,Storm,2015-0016-MDG,7761,"Fenoarivo Atsinanana, Madagascar"
+16,1,2015,Analamanga,Madagascar,Storm,2015-0016-MDG,33675,"Antananarivo, Madagascar"
+16,1,2015,Atsimo-Atsinanana,Madagascar,Storm,2015-0016-MDG,9555,"Farafangana, Madagascar"
+16,1,2015,Betsiboka,Madagascar,Storm,2015-0016-MDG,3672,"Maevatanana, Madagascar"
+16,1,2015,Boeny,Madagascar,Storm,2015-0016-MDG,8667,"Mahajanga, Madagascar"
+16,1,2015,Bongolava,Madagascar,Storm,2015-0016-MDG,6277,"Tsiroanomandidy, Madagascar"
+16,1,2015,Diana,Madagascar,Storm,2015-0016-MDG,8281,"Antsiranana, Madagascar"
+16,1,2015,Haute Matsiatra,Madagascar,Storm,2015-0016-MDG,13470,"Fianarantsoa, Madagascar"
+16,1,2015,Itasy,Madagascar,Storm,2015-0016-MDG,8358,"Miarinarivo, Madagascar"
+16,1,2015,Melaky,Madagascar,Storm,2015-0016-MDG,2883,"Maintirano, Madagascar"
+16,1,2015,Menabe,Madagascar,Storm,2015-0016-MDG,6520,"Morondava, Madagascar"
+16,1,2015,Sofia,Madagascar,Storm,2015-0016-MDG,13963,"Antsohihy, Madagascar"
+16,1,2015,Vakinankaratra,Madagascar,Storm,2015-0016-MDG,19306,"Antsirabe, Madagascar"
+16,1,2015,Vatovavy,Madagascar,Storm,2015-0016-MDG,6568,"Mananjary, Madagascar"
+16,1,2015,Vatovavy-Fitovinany,Madagascar,Storm,2015-0016-MDG,13364,"Manakara, Madagascar"
+17,2,2000,Amoron'i Mania,Madagascar,Storm,2000-0107-MDG,48065,"Fenoarivo Atsinanana, Madagascar"
+17,2,2000,Analamanga,Madagascar,Storm,2000-0107-MDG,208538,"Antananarivo, Madagascar"
+17,2,2000,Analanjirofo,Madagascar,Storm,2000-0107-MDG,66418,"Fenoarivo Atsinanana, Madagascar"
+17,2,2000,Atsimo-Andrefana,Madagascar,Storm,2000-0107-MDG,103694,"Toliara, Madagascar"
+17,2,2000,Atsinanana,Madagascar,Storm,2000-0107-MDG,85557,"Toamasina, Madagascar"
+17,2,2000,Menabe,Madagascar,Storm,2000-0107-MDG,40379,"Morondava, Madagascar"
+17,2,2000,Sava,Madagascar,Storm,2000-0107-MDG,64727,"Sambava, Madagascar"
+17,2,2000,Vakinankaratra,Madagascar,Storm,2000-0107-MDG,119560,"Antsirabe, Madagascar"
+17,2,2008,Alaotra-Mangoro,Madagascar,Storm,2008-0070-MDG,40307,"Ambatondrazaka, Madagascar"
+17,2,2008,Analamanga,Madagascar,Storm,2008-0070-MDG,116155,"Antananarivo, Madagascar"
+17,2,2008,Analanjirofo,Madagascar,Storm,2008-0070-MDG,36994,"Fenoarivo Atsinanana, Madagascar"
+17,2,2008,Atsimo-Atsinanana,Madagascar,Storm,2008-0070-MDG,32960,"Farafangana, Madagascar"
+17,2,2008,Atsinanana,Madagascar,Storm,2008-0070-MDG,47655,"Toamasina, Madagascar"
+17,2,2008,Betsiboka,Madagascar,Storm,2008-0070-MDG,12667,"Maevatanana, Madagascar"
+17,2,2008,Boeny,Madagascar,Storm,2008-0070-MDG,29894,"Mahajanga, Madagascar"
+17,2,2008,Bongolava,Madagascar,Storm,2008-0070-MDG,21653,"Tsiroanomandidy, Madagascar"
+17,2,2008,Haute Matsiatra,Madagascar,Storm,2008-0070-MDG,46463,"Fianarantsoa, Madagascar"
+17,2,2008,Menabe,Madagascar,Storm,2008-0070-MDG,22491,"Morondava, Madagascar"
+17,2,2008,Sofia,Madagascar,Storm,2008-0070-MDG,48163,"Antsohihy, Madagascar"
+17,2,2008,Vatovavy,Madagascar,Storm,2008-0070-MDG,22655,"Mananjary, Madagascar"
+17,2,2008,Vatovavy-Fitovinany,Madagascar,Storm,2008-0070-MDG,46097,"Manakara, Madagascar"
+17,3,2009,Androy,Madagascar,Storm,2009-0123-MDG,724,"Ambovombe-Androy, Madagascar"
+17,3,2009,Anosy,Madagascar,Storm,2009-0123-MDG,649,"Taolagnaro, Madagascar"
+17,3,2009,Atsimo-Andrefana,Madagascar,Storm,2009-0123-MDG,1442,"Toliara, Madagascar"
+17,3,2009,Menabe,Madagascar,Storm,2009-0123-MDG,561,"Morondava, Madagascar"
+18,1,2003,Analamanga,Madagascar,Flood,2003-0037-MDG,9126,"Antananarivo, Madagascar"
+18,1,2003,Atsimo-Atsinanana,Madagascar,Flood,2003-0037-MDG,2590,"Farafangana, Madagascar"
+18,1,2003,Atsinanana,Madagascar,Flood,2003-0037-MDG,3744,"Toamasina, Madagascar"
+18,1,2003,Betsiboka,Madagascar,Flood,2003-0037-MDG,995,"Maevatanana, Madagascar"
+18,1,2003,Boeny,Madagascar,Flood,2003-0037-MDG,2349,"Mahajanga, Madagascar"
+18,1,2003,Melaky,Madagascar,Flood,2003-0037-MDG,781,"Maintirano, Madagascar"
+18,1,2003,Sofia,Madagascar,Flood,2003-0037-MDG,3784,"Antsohihy, Madagascar"
+18,2,2021,Alaotra-Mangoro,Madagascar,Flood,2021-0090-MDG,299,"Ambatondrazaka, Madagascar"
+18,2,2021,Analamanga,Madagascar,Flood,2021-0090-MDG,861,"Antananarivo, Madagascar"
+18,2,2021,Melaky,Madagascar,Flood,2021-0090-MDG,74,"Maintirano, Madagascar"
+18,2,2021,Menabe,Madagascar,Flood,2021-0090-MDG,167,"Morondava, Madagascar"
+19,1,2009,Amoron'i Mania,Madagascar,Storm,2009-0047-MDG,7672,"Fenoarivo Atsinanana, Madagascar"
+19,1,2009,Analanjirofo,Madagascar,Storm,2009-0047-MDG,10601,"Fenoarivo Atsinanana, Madagascar"
+19,1,2009,Atsinanana,Madagascar,Storm,2009-0047-MDG,13656,"Toamasina, Madagascar"
+19,1,2009,Menabe,Madagascar,Storm,2009-0047-MDG,6445,"Morondava, Madagascar"
+19,1,2009,Sava,Madagascar,Storm,2009-0047-MDG,10331,"Sambava, Madagascar"
+19,1,2009,Sofia,Madagascar,Storm,2009-0047-MDG,13801,"Antsohihy, Madagascar"
+19,8,2017,Alaotra-Mangoro,Madagascar,Epidemic,2017-0411-MDG,203,"Ambatondrazaka, Madagascar"
+19,8,2017,Amoron'i Mania,Madagascar,Epidemic,2017-0411-MDG,135,"Fenoarivo Atsinanana, Madagascar"
+19,8,2017,Analamanga,Madagascar,Epidemic,2017-0411-MDG,585,"Antananarivo, Madagascar"
+19,8,2017,Atsinanana,Madagascar,Epidemic,2017-0411-MDG,240,"Toamasina, Madagascar"
+19,8,2017,Betsiboka,Madagascar,Epidemic,2017-0411-MDG,64,"Maevatanana, Madagascar"
+19,8,2017,Boeny,Madagascar,Epidemic,2017-0411-MDG,151,"Mahajanga, Madagascar"
+19,8,2017,Bongolava,Madagascar,Epidemic,2017-0411-MDG,109,"Tsiroanomandidy, Madagascar"
+19,8,2017,Haute Matsiatra,Madagascar,Epidemic,2017-0411-MDG,234,"Fianarantsoa, Madagascar"
+19,8,2017,Itasy,Madagascar,Epidemic,2017-0411-MDG,145,"Miarinarivo, Madagascar"
+19,8,2017,Sava,Madagascar,Epidemic,2017-0411-MDG,182,"Sambava, Madagascar"
+19,8,2017,Vakinankaratra,Madagascar,Epidemic,2017-0411-MDG,336,"Antsirabe, Madagascar"
+20,12,1981,Diana,Madagascar,Storm,1981-0110-MDG,118000,"Antsiranana, Madagascar"
+22,1,2005,Androy,Madagascar,Storm,2005-0048-MDG,2669,"Ambovombe-Androy, Madagascar"
+22,1,2005,Atsimo-Andrefana,Madagascar,Storm,2005-0048-MDG,5316,"Toliara, Madagascar"
+22,2,2013,Analamanga,Madagascar,Storm,2013-0032-MDG,23823,"Antananarivo, Madagascar"
+22,2,2013,Atsimo-Andrefana,Madagascar,Storm,2013-0032-MDG,11846,"Toliara, Madagascar"
+22,2,2013,Menabe,Madagascar,Storm,2013-0032-MDG,4613,"Morondava, Madagascar"
+23,1,2021,Analanjirofo,Madagascar,Storm,2021-0036-MDG,306,"Fenoarivo Atsinanana, Madagascar"
+23,1,2021,Atsinanana,Madagascar,Storm,2021-0036-MDG,395,"Toamasina, Madagascar"
+23,1,2021,Sava,Madagascar,Storm,2021-0036-MDG,299,"Sambava, Madagascar"
+24,1,1997,Atsimo-Atsinanana,Madagascar,Storm,1997-0013-MDG,250148,"Farafangana, Madagascar"
+24,1,1997,Vatovavy-Fitovinany,Madagascar,Storm,1997-0013-MDG,349852,"Manakara, Madagascar"
+24,1,2012,Atsimo-Andrefana,Madagascar,Storm,2012-0005-MDG,192,"Toliara, Madagascar"
+24,1,2012,Melaky,Madagascar,Storm,2012-0005-MDG,33,"Maintirano, Madagascar"
+24,1,2012,Menabe,Madagascar,Storm,2012-0005-MDG,75,"Morondava, Madagascar"
+24,3,1999,Analamanga,Madagascar,Epidemic,1999-0490-MDG,5398,"Antananarivo, Madagascar"
+24,3,1999,Atsimo-Andrefana,Madagascar,Epidemic,1999-0490-MDG,2684,"Toliara, Madagascar"
+24,3,1999,Atsinanana,Madagascar,Epidemic,1999-0490-MDG,2215,"Toamasina, Madagascar"
+24,3,1999,Boeny,Madagascar,Epidemic,1999-0490-MDG,1389,"Mahajanga, Madagascar"
+24,3,1999,Diana,Madagascar,Epidemic,1999-0490-MDG,1327,"Antsiranana, Madagascar"
+24,3,1999,Haute Matsiatra,Madagascar,Epidemic,1999-0490-MDG,2159,"Fianarantsoa, Madagascar"
+24,3,2005,Anosy,Madagascar,Flood,2005-0165-MDG,900,"Taolagnaro, Madagascar"
+25,12,2006,Boeny,Madagascar,Storm,2006-0687-MDG,138,"Mahajanga, Madagascar"
+25,12,2006,Sava,Madagascar,Storm,2006-0687-MDG,166,"Sambava, Madagascar"
+26,1,2004,Analamanga,Madagascar,Storm,2004-0029-MDG,17268,"Antananarivo, Madagascar"
+26,1,2004,Atsimo-Andrefana,Madagascar,Storm,2004-0029-MDG,8586,"Toliara, Madagascar"
+26,1,2004,Atsinanana,Madagascar,Storm,2004-0029-MDG,7084,"Toamasina, Madagascar"
+26,1,2004,Boeny,Madagascar,Storm,2004-0029-MDG,4444,"Mahajanga, Madagascar"
+26,1,2004,Haute Matsiatra,Madagascar,Storm,2004-0029-MDG,6907,"Fianarantsoa, Madagascar"
+26,2,2012,Alaotra-Mangoro,Madagascar,Storm,2012-0056-MDG,8883,"Ambatondrazaka, Madagascar"
+26,2,2012,Analamanga,Madagascar,Storm,2012-0056-MDG,25598,"Antananarivo, Madagascar"
+26,2,2012,Analanjirofo,Madagascar,Storm,2012-0056-MDG,8153,"Fenoarivo Atsinanana, Madagascar"
+26,2,2012,Anosy,Madagascar,Storm,2012-0056-MDG,5726,"Taolagnaro, Madagascar"
+26,2,2012,Atsimo-Atsinanana,Madagascar,Storm,2012-0056-MDG,7264,"Farafangana, Madagascar"
+26,2,2012,Diana,Madagascar,Storm,2012-0056-MDG,6295,"Antsiranana, Madagascar"
+26,2,2012,Sava,Madagascar,Storm,2012-0056-MDG,7945,"Sambava, Madagascar"
+26,2,2012,Vatovavy,Madagascar,Storm,2012-0056-MDG,4993,"Mananjary, Madagascar"
+26,2,2012,Vatovavy-Fitovinany,Madagascar,Storm,2012-0056-MDG,10159,"Manakara, Madagascar"
+27,2,2015,Analamanga,Madagascar,Flood,2015-0057-MDG,24000,"Antananarivo, Madagascar"
+27,1,2008,Analamanga,Madagascar,Storm,2008-0043-MDG,6413,"Antananarivo, Madagascar"
+27,1,2008,Boeny,Madagascar,Storm,2008-0043-MDG,1651,"Mahajanga, Madagascar"
+27,1,2008,Melaky,Madagascar,Storm,2008-0043-MDG,549,"Maintirano, Madagascar"
+28,1,1982,Atsimo-Andrefana,Madagascar,Storm,1982-0147-MDG,34763,"Toliara, Madagascar"
+28,1,1982,Menabe,Madagascar,Storm,1982-0147-MDG,13537,"Morondava, Madagascar"
+28,1,1982,Sava,Madagascar,Storm,1982-0147-MDG,21700,"Sambava, Madagascar"
+29,12,1986,Analamanga,Madagascar,Flood,1986-0144-MDG,28223,"Antananarivo, Madagascar"
+29,1,2003,Analanjirofo,Madagascar,Storm,2003-0065-MDG,83,"Fenoarivo Atsinanana, Madagascar"
+29,1,2003,Atsimo-Atsinanana,Madagascar,Storm,2003-0065-MDG,74,"Farafangana, Madagascar"
+29,1,2003,Atsinanana,Madagascar,Storm,2003-0065-MDG,107,"Toamasina, Madagascar"
+29,1,2003,Sava,Madagascar,Storm,2003-0065-MDG,81,"Sambava, Madagascar"
+29,1,2003,Vatovavy,Madagascar,Storm,2003-0065-MDG,51,"Mananjary, Madagascar"
+29,1,2003,Vatovavy-Fitovinany,Madagascar,Storm,2003-0065-MDG,104,"Manakara, Madagascar"
+29,3,2014,Betsiboka,Madagascar,Storm,2014-0092-MDG,170,"Maevatanana, Madagascar"
+29,3,2014,Boeny,Madagascar,Storm,2014-0092-MDG,402,"Mahajanga, Madagascar"
+29,3,2014,Diana,Madagascar,Storm,2014-0092-MDG,384,"Antsiranana, Madagascar"
+29,3,2014,Melaky,Madagascar,Storm,2014-0092-MDG,134,"Maintirano, Madagascar"
+29,3,2014,Sofia,Madagascar,Storm,2014-0092-MDG,647,"Antsohihy, Madagascar"
+30,1,2013,Analanjirofo,Madagascar,Storm,2013-0026-MDG,765,"Fenoarivo Atsinanana, Madagascar"
+30,1,2013,Atsimo-Atsinanana,Madagascar,Storm,2013-0026-MDG,681,"Farafangana, Madagascar"
+30,1,2013,Atsinanana,Madagascar,Storm,2013-0026-MDG,985,"Toamasina, Madagascar"
+30,1,2013,Sava,Madagascar,Storm,2013-0026-MDG,745,"Sambava, Madagascar"
+30,1,2013,Vatovavy,Madagascar,Storm,2013-0026-MDG,468,"Mananjary, Madagascar"
+30,1,2013,Vatovavy-Fitovinany,Madagascar,Storm,2013-0026-MDG,953,"Manakara, Madagascar"
+31,12,1989,Analanjirofo,Madagascar,Storm,1989-0122-MDG,10471,"Fenoarivo Atsinanana, Madagascar"
+31,12,1989,Boeny,Madagascar,Storm,1989-0122-MDG,8461,"Mahajanga, Madagascar"
+31,12,1989,Haute Matsiatra,Madagascar,Storm,1989-0122-MDG,13151,"Fianarantsoa, Madagascar"
+31,12,1989,Ihorombe,Madagascar,Storm,1989-0122-MDG,3803,"Ihosy, Madagascar"
+31,12,1989,Vatovavy,Madagascar,Storm,1989-0122-MDG,6412,"Mananjary, Madagascar"
+31,12,1989,Vatovavy-Fitovinany,Madagascar,Storm,1989-0122-MDG,13047,"Manakara, Madagascar"
+0,2,1998,Analamanga,Madagascar,Flood,1998-0086-MDG,7798,"Antananarivo, Madagascar"
+0,2,1998,Atsimo-Andrefana,Madagascar,Flood,1998-0086-MDG,3877,"Toliara, Madagascar"
+0,2,1998,Atsinanana,Madagascar,Flood,1998-0086-MDG,3199,"Toamasina, Madagascar"
+0,2,1998,Boeny,Madagascar,Flood,1998-0086-MDG,2007,"Mahajanga, Madagascar"
+0,2,1998,Haute Matsiatra,Madagascar,Flood,1998-0086-MDG,3119,"Fianarantsoa, Madagascar"
+0,12,1999,Analamanga,Madagascar,Epidemic,1999-0710-MDG,1527,"Antananarivo, Madagascar"
+0,12,1999,Atsimo-Andrefana,Madagascar,Epidemic,1999-0710-MDG,759,"Toliara, Madagascar"
+0,12,1999,Boeny,Madagascar,Epidemic,1999-0710-MDG,393,"Mahajanga, Madagascar"
+0,12,1999,Diana,Madagascar,Epidemic,1999-0710-MDG,376,"Antsiranana, Madagascar"
+0,1,2008,Alaotra-Mangoro,Madagascar,Epidemic,2008-0345-MDG,75,"Ambatondrazaka, Madagascar"
+0,1,2008,Analamanga,Madagascar,Epidemic,2008-0345-MDG,217,"Antananarivo, Madagascar"
+0,1,2008,Anosy,Madagascar,Epidemic,2008-0345-MDG,49,"Taolagnaro, Madagascar"
+0,1,2008,Itasy,Madagascar,Epidemic,2008-0345-MDG,54,"Miarinarivo, Madagascar"
+0,1,2008,Vakinankaratra,Madagascar,Epidemic,2008-0345-MDG,125,"Antsirabe, Madagascar"
+0,10,2009,Vatovavy,Madagascar,Epidemic,2009-0634-MDG,231,"Mananjary, Madagascar"
+0,10,2009,Vatovavy-Fitovinany,Madagascar,Epidemic,2009-0634-MDG,471,"Manakara, Madagascar"
diff --git a/optimization202/ESUPS_case_study/data/madagascar/distanceMatrix.csv b/optimization202/ESUPS_case_study/data/madagascar/distanceMatrix.csv
new file mode 100644
index 0000000..cdccf74
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/distanceMatrix.csv
@@ -0,0 +1,622 @@
+depotCity,depotCountry,gglCountryAscii,depotGglAddressAscii,depotGglLat,depotGglLong,emdatCity,emdatCountry,gglCountryAscii,disasterGglAddressAscii,disasterGglLat,disasterGglLong,drivingTime_hrs,distance_km
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,0,0
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,11,518
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,6,269
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,11,496
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,29,1156
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,22,1187
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,18,841
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,8,398
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,11,588
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,16,843
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,9,479
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,21,889
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,14,669
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,17,863
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,8,359
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,14.32,771
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,17,956
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,23,1055
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,10,483
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,9,427
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,14,609
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,16,740
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,11,517
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,0,0
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,5,255
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,15,695
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,19,735
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,11,670
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,8,420
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,12,598
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,11,572
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,15,827
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,6,350
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,30,1375
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,3,152
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,6,345
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,5,275
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,11.27,642
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,6,439
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,31,1541
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,18,951
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,2,90
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,5,274
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,6,319
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,6,269
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,5,255
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,0,0
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,10,447
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,24,991
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,16,925
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,13,676
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,7,349
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,5,319
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,10,574
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,3,211
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,25,1121
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,8,407
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,12,601
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,2,90
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,8.33,502
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,12,694
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,26,1288
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,13,697
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,3,165
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,11,529
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,11,574
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,11,496
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,15,695
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,10,447
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,0,0
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,29,1120
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,25,1257
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,18,805
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,2,102
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,16,766
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,20,1021
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,14,657
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,32,1392
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,17,739
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,20,932
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,12,536
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,18.57,949
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,22,1134
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,34,1558
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,21,986
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,13,605
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,14,573
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,16,704
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,13.75,583
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,17.75,783
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,12.75,534
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,2.75,88
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,31.75,1208
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,27.75,1344
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,20.75,893
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,4.75,190
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,18.75,854
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,23.75,1109
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,16.75,745
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,35.75,1479
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,20.75,826
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,23.75,1020
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,14.75,624
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,21.35,1037
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,24.75,1222
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,36.75,1645
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,23.75,1073
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,15.75,692
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,16.75,661
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,18.75,792
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,23,790
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,28,989
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,23,741
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,12,294
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,42,1414
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,38,1551
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,31,1099
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,15,396
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,28,1060
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,33,1315
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,26,951
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,45,1686
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,30,1033
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,33,1226
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,24,830
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,31,1241
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,34,1428
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,47,1852
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,33,1280
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,26,899
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,27,867
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,29,998
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,17,683
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,22,883
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,17,634
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,6,188
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,36,1308
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,32,1444
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,25,993
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,9,290
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,22,954
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,27,1209
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,20,845
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,39,1579
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,24,926
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,27,1120
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,18,724
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,25,1137
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,28,1322
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,41,1745
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,27,1173
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,20,792
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,21,761
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,23,892
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,12,558
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,16,758
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,11,509
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,1,63
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,30,1183
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,26,1319
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,19,868
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,3,165
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,17,829
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,22,1084
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,15,720
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,34,1454
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,19,801
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,22,995
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,13,599
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,19.6,1012
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,23,1197
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,35,1620
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,22,1048
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,14,667
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,15,636
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,17,767
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,30,1252
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,19,735
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,24,990
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,29,1138
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,0,0
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,15,622
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,11,315
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,27,1040
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,30,1307
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,34,1562
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,25,1085
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,49,2110
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,16,701
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,12,507
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,24,1010
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,30,1377
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,23,1167
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,50,2276
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,37,1686
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,20,825
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,15,579
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,12,417
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,22,1187
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,11,670
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,16,925
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,26,1275
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,15,623
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,0,0
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,9,597
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,23,1177
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,22,1242
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,27,1497
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,18,1020
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,41,2045
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,8,519
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,4,325
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,17,945
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,22.5,1312
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,15,985
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,43,2211
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,29,1621
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,13,760
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,11,716
+Toliara,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,11,698
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,27,1336
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,15.6,815
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,21.35,1078
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,30,1424
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,8.68,321
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,6.7,285
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,13.8,746
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,28,1325
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,27,1391
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,32,1644
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,22.5,1169
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,46,2187
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,12.37,667
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,9.4,475
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,21.4,1093
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,27,1453
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,20.1,1136
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,48,2360
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,34,1768
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,17.68,908
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,16.5,866
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,15.5,848
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,22.83,1178
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,11.53,656
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,17.33,919
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,26,1264
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,12.96,531
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,2.68,126
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,9.7,587
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,23.71,1166
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,22.8,1232
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,28,1485
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,18.46,1010
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,42,2028
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,8.3,508
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,5.25,316
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,17.38,934
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,26,1361
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,16.01,977
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,44,2201
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,30,1608
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,13.61,749
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,12.35,707
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,11.43,689
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,30.75,1064
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,19.5,543
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,25.18,806
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,30.17,949
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,19.45,292
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,20.75,681
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,11.6,125.9
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,27.55,850
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,30.69,1118
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,35,1372
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,26.32,896
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,50,1914
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,18.02,471
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,15.95,356
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,25.25,821
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,30.75,1258
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,24.92,937
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,51,2088
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,38,1495
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,21.5,636
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,16.17,392
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,13.35,229
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,19,937
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,7,420
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,13,675
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,18,823
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,11,315
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,9,597
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,0,0
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,16,725
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,19,992
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,23,1247
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,14,770
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,38,1795
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,6,344
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,4,272
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,13,695
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,19.08,1055
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,13,808
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,39,1961
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,26,1371
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,9,510
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,4,264
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,1,101
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,20,1011
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,9,494
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,14,749
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,20,897
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,9,241
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,10,630
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,1,74
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,17,799
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,20,1066
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,25,1321
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,16,844
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,39,1869
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,7,418
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,5,306
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,15,769
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,20.67,1130
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,14,883
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,41,2035
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,27,1445
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,11,584
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,6,338
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,3,175
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,8,398
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,12,598
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,7,349
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,2,102
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,26,1023
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,23,1159
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,15,707
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,0,0
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,13,669
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,18,924
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,11,560
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,30,1294
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,15,641
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,18,835
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,9,439
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,15.75,844
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,19,1036
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,31,1460
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,18,888
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,10,507
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,11,476
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,14,606
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,11,588
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,11,572
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,5,319
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,16,766
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,30,1308
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,22,1242
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,19,992
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,13,668
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,0,0
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,4,254
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,9,522
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,19,802
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,14,724
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,17,917
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,7,401
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,13.57,807
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,17,1011
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,20,968
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,7,378
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,9,482
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,16,846
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,17,891
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,16,843
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,15,827
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,10,574
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,20,1021
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,34,1563
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,27,1497
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,23,1248
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,18,923
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,4,255
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,0,0
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,13,777
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,19,811
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,19,979
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,22,1173
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,12,656
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,18.28,1060
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,22,1266
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,21,977
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,7,387
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,13,737
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,21,1101
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,22,1146
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,14,650
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,22,1136
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,17,883
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,25,1153
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,41,1872
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,34,1806
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,30,1556
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,22,1055
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,11,563
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,12,572
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,20,1086
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,7,239
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,26,1288
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,29,1481
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,19,965
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,25,1372
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,29,1575
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,9,405
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,4,188
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,20,1045
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,28,1410
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,29,1455
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,7,358
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,5,275
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,1,90
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,12,536
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,24,1010
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,17,944
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,13,695
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,9,439
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,7,401
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,12,656
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,1,123
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,26,1204
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,9,427
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,12,620
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,0,0
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,6.3,407
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,12,714
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,28,1370
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,14,780
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,3,184
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,11,548
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,12,594
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,14.25,772
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,11.2,644
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,8.3,506
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,18.2,949
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,30,1376
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,22.5,1310
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,19.25,1062
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,15.75,851
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,13.65,814
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,18.5,1067
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,4.5,292
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,33,1610
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,14.7,794
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,17.7,986
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,6.4,414
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,0,0
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,17.75,1083
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,35,1783
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,20.75,1191
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,9.4,553
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,16.9,917
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,17.5,960
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,16,907
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,6,439
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,11,645
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,21,1085
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,23,1167
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,15,984
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,13,809
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,18,987
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,16,962
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,21,1217
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,12,740
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,36,1765
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,8,540
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,10,660
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,11,665
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,17.07,1027
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,0,0
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,37,1931
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,24,1341
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,7,481
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,10,662
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,11,708
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,24,1134
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,33,1620
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,27,1367
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,35,1636
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,52,2355
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,44,2290
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,41,2040
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,33,1539
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,22,1047
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,22,1056
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,31,1569
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,12,507
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,36,1772
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,39,1965
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,29,1449
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,36,1857
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,39,2059
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,1,78
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,15,672
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,31,1529
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,38,1894
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,39,1939
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,23,1055
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,31,1541
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,26,1288
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,34,1558
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,50,2277
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,43,2211
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,39,1962
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,31,1460
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,20,968
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,21,977
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,29,1491
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,10,429
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,35,1693
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,38,1887
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,28,1370
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,34,1777
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,38,1980
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,0,0
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,13,593
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,29,1451
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,37,1815
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,38,1860
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,10,483
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,18,952
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,13,698
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,21,986
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,37,1687
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,29,1621
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,26,1372
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,18,888
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,7,379
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,7,387
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,16,901
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,12,427
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,21,1103
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,24,1297
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,14,780
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,20.67,1184
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,24,1390
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,13,593
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,0,0
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,16,861
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,23,1225
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,24,1271
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,17,836
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,6,319
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,11,574
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,17,721
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,12,417
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,11,698
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,1,101
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,14,624
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,17,891
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,22,1146
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,13,669
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,36,1694
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,4,243
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,6,373
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,12,594
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,17.38,952
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,11,707
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,38,1860
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,24,1270
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,8,409
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,2,163
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,0,0
+Ambatondrazaka,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,26,1262
+Ambositra,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,15,745
+Antananarivo Renivohitra,Madagascar,Madagascar,"Antananarivo Renivohitra, Madagascar",-18.9085,47.5375,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,21,1000
+Fenerive Est,Madagascar,Madagascar,"Fenerive Est, Madagascar",-17.3843,49.4098,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,29,1332
+Sainte-Marie,Madagascar,Madagascar,"Sainte-Marie, Madagascar",-16.9167,49.9,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,31.75,1420
+Maroantsetra,Madagascar,Madagascar,"Maroantsetra, Madagascar",-15.4309,49.7583,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,42,1626
+Mananara,Madagascar,Madagascar,"Mananara, Madagascar",-16.1702,49.7741,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,36,1520
+Soanierana Ivongo,Madagascar,Madagascar,"Soanierana Ivongo, Madagascar",-16.9261,49.5871,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,30,1395
+Taolagnaro,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,3,107
+Ampanihy,Madagascar,Madagascar,"Ampanihy, Madagascar",-24.69353,44.74662,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,5.13,214
+Toliary I,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,11,499
+Benenitra,Madagascar,Madagascar,"Benenitra, Madagascar",-23.4522,45.0781,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,9.46,424
+Midongy,Madagascar,Madagascar,"Midongy, Madagascar",-23.42605,47.05908,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,22.5,469
+Farafangana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,13,420
+Vangaindrano,Madagascar,Madagascar,"Vangaindrano, Madagascar",-23.3505,47.6088,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,12,345
+Toamasina I,Madagascar,Madagascar,"Toamasina I, Madagascar",-18.1499,49.4023,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,27,1234
+Maevatanana,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,26,1317
+Mahajanga I,Madagascar,Madagascar,"Mahajanga I, Madagascar",-15.7167,46.3167,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,31,1572
+Ambanja,Madagascar,Madagascar,"Ambanja, Madagascar",-13.6804,48.4555,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,38,1881
+Miarinarivo,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,21,1020
+Maintirano,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,26,1335
+Morondava,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,19,1059
+Antalaha,Madagascar,Madagascar,"Antalaha, Madagascar",-14.8833,50.2833,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,48,2364
+Sambava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,47,2286
+Antsohihy,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,33,1696
+Manakara,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,15,521
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Alaotra-Mangoro,Madagascar,Madagascar,"Ambatondrazaka, Madagascar",-17.8237,48.4263,26,1262
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Amoron'i Mania,Madagascar,Madagascar,"Ambositra, Madagascar",-20.5167,47.25,15,744
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Analamanga,Madagascar,Madagascar,"Antananarivo, Madagascar",-18.9085,47.5375,21,1000
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Analanjirofo,Madagascar,Madagascar,"Fenoarivo Atsinanana, Madagascar",-17.3843,49.4098,30,1349
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Androy,Madagascar,Madagascar,"Ambovombe-Androy, Madagascar",-25.17613271,46.08937803,0,0
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Anosy,Madagascar,Madagascar,"Taolagnaro, Madagascar",-25.0316,46.99,3,107
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Atsimo-Andrefana,Madagascar,Madagascar,"Toliara, Madagascar",-23.35,43.6667,11,499
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Atsimo-Atsinanana,Madagascar,Madagascar,"Farafangana, Madagascar",-22.8167,47.8167,13,420
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Atsinanana,Madagascar,Madagascar,"Toamasina, Madagascar",-18.1499,49.4023,27,1252
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Betsiboka,Madagascar,Madagascar,"Maevatanana, Madagascar",-16.9504,46.8281,26,1317
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Boeny,Madagascar,Madagascar,"Mahajanga, Madagascar",-15.7167,46.3167,31,1572
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Bongolava,Madagascar,Madagascar,"Tsiroanomandidy, Madagascar",-18.7698,46.05,22,1095
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Diana,Madagascar,Madagascar,"Antsiranana, Madagascar",-12.2667,49.2833,45,2120
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Haute Matsiatra,Madagascar,Madagascar,"Fianarantsoa, Madagascar",-21.447,47.0872,12,593
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Ihorombe,Madagascar,Madagascar,"Ihosy, Madagascar",-22.3961,46.1217,9,400
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Itasy,Madagascar,Madagascar,"Miarinarivo, Madagascar",-18.9608,46.9036,21,1019
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Melaky,Madagascar,Madagascar,"Maintirano, Madagascar",-18.0646,44.0295,26,1387
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Menabe,Madagascar,Madagascar,"Morondava, Madagascar",-20.2847,44.3175,19,1059
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Sava,Madagascar,Madagascar,"Sambava, Madagascar",-14.2667,50.1667,47,2286
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Sofia,Madagascar,Madagascar,"Antsohihy, Madagascar",-14.8762,47.9835,33,1696
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Vakinankaratra,Madagascar,Madagascar,"Antsirabe, Madagascar",-19.8667,47.0333,17,835
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Vatovavy,Madagascar,Madagascar,"Mananjary, Madagascar",-21.2367,48.3461,16,791
+Ambovombe,Madagascar,Madagascar,"Ambovombe, Madagascar",-25.17613271,46.08937803,Vatovavy-Fitovinany,Madagascar,Madagascar,"Manakara, Madagascar",-22.15,48,15,521
diff --git a/optimization202/ESUPS_case_study/data/madagascar/inventory/actual.csv b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual.csv
new file mode 100644
index 0000000..ff77592
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual.csv
@@ -0,0 +1,114 @@
+ItemName,Warehouse(Region),gglAddress,gglCountry,Total
+Blankets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,3400
+Blankets,Africa,"Toamasina I, Madagascar",Madagascar,5000
+Buckets,Africa,"Ambanja, Madagascar",Madagascar,375
+Buckets,Africa,"Ambatondrazaka, Madagascar",Madagascar,26
+Buckets,Africa,"Ambositra, Madagascar",Madagascar,41
+Buckets,Africa,"Ambovombe, Madagascar",Madagascar,2322
+Buckets,Africa,"Ampanihy, Madagascar",Madagascar,1027
+Buckets,Africa,"Antalaha, Madagascar",Madagascar,5700
+Buckets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,9046
+Buckets,Africa,"Antsohihy, Madagascar",Madagascar,610
+Buckets,Africa,"Farafangana, Madagascar",Madagascar,3331
+Buckets,Africa,"Fenerive Est, Madagascar",Madagascar,2762
+Buckets,Africa,"Mahajanga I, Madagascar",Madagascar,150
+Buckets,Africa,"Maintirano, Madagascar",Madagascar,2460
+Buckets,Africa,"Manakara, Madagascar",Madagascar,4235
+Buckets,Africa,"Maroantsetra, Madagascar",Madagascar,1870
+Buckets,Africa,"Miarinarivo, Madagascar",Madagascar,3
+Buckets,Africa,"Midongy, Madagascar",Madagascar,1870
+Buckets,Africa,"Morondava, Madagascar",Madagascar,736
+Buckets,Africa,"Sainte-Marie, Madagascar",Madagascar,1682
+Buckets,Africa,"Soanierana Ivongo, Madagascar",Madagascar,375
+Buckets,Africa,"Toamasina I, Madagascar",Madagascar,1580
+Buckets,Africa,"Toliara, Madagascar",Madagascar,610
+Clothes,Africa,"Ambanja, Madagascar",Madagascar,80
+Clothes,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,3200
+Clothes,Africa,"Soanierana Ivongo, Madagascar",Madagascar,80
+HygieneAndDignityKits,Africa,"Ambovombe, Madagascar",Madagascar,300
+HygieneAndDignityKits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,2500
+HygieneAndDignityKits,Africa,"Maroantsetra, Madagascar",Madagascar,276
+Kitchenset,Africa,"Ambanja, Madagascar",Madagascar,375
+Kitchenset,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,2581
+Kitchenset,Africa,"Mahajanga I, Madagascar",Madagascar,150
+Kitchenset,Africa,"Morondava, Madagascar",Madagascar,2280
+Kitchenset,Africa,"Soanierana Ivongo, Madagascar",Madagascar,375
+Mosquitonets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,3052
+Mosquitonets,Africa,"Fenerive Est, Madagascar",Madagascar,1000
+Mosquitonets,Africa,"Toamasina I, Madagascar",Madagascar,25300
+Otherlampslanterns,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,4
+Otherlampslanterns,Africa,"Toamasina I, Madagascar",Madagascar,3
+PersonalProtectionEquipmentkit(PPE),Africa,"Ambanja, Madagascar",Madagascar,80
+PersonalProtectionEquipmentkit(PPE),Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,4840
+PersonalProtectionEquipmentkit(PPE),Africa,"Fenerive Est, Madagascar",Madagascar,1352
+PersonalProtectionEquipmentkit(PPE),Africa,"Soanierana Ivongo, Madagascar",Madagascar,80
+PersonalProtectionEquipmentkit(PPE),Africa,"Toamasina I, Madagascar",Madagascar,361
+PersonalProtectionEquipmentkit(PPE),Africa,"Toliara, Madagascar",Madagascar,50
+SafeDeliverykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,40
+SchoolPlaykits,Africa,"Ambovombe, Madagascar",Madagascar,295
+SchoolPlaykits,Africa,"Antalaha, Madagascar",Madagascar,750
+SchoolPlaykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,77
+SchoolPlaykits,Africa,"Farafangana, Madagascar",Madagascar,273
+SchoolPlaykits,Africa,"Fenerive Est, Madagascar",Madagascar,131
+SchoolPlaykits,Africa,"Maevatanana, Madagascar",Madagascar,60
+SchoolPlaykits,Africa,"Mahajanga I, Madagascar",Madagascar,1684
+SchoolPlaykits,Africa,"Maintirano, Madagascar",Madagascar,125
+SchoolPlaykits,Africa,"Manakara, Madagascar",Madagascar,300
+SchoolPlaykits,Africa,"Mananara, Madagascar",Madagascar,171
+SchoolPlaykits,Africa,"Maroantsetra, Madagascar",Madagascar,48
+SchoolPlaykits,Africa,"Sambava, Madagascar",Madagascar,75
+SchoolPlaykits,Africa,"Taolagnaro, Madagascar",Madagascar,218
+SchoolPlaykits,Africa,"Toamasina I, Madagascar",Madagascar,65
+SchoolPlaykits,Africa,"Toliara, Madagascar",Madagascar,144
+ShelterToolKit,Africa,"Ambanja, Madagascar",Madagascar,225
+ShelterToolKit,Africa,"Antalaha, Madagascar",Madagascar,600
+ShelterToolKit,Africa,"Soanierana Ivongo, Madagascar",Madagascar,225
+Sleepingmats,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,4
+Tarpaulins,Africa,"Ambanja, Madagascar",Madagascar,450
+Tarpaulins,Africa,"Ambositra, Madagascar",Madagascar,10
+Tarpaulins,Africa,"Ambovombe, Madagascar",Madagascar,156
+Tarpaulins,Africa,"Antalaha, Madagascar",Madagascar,996
+Tarpaulins,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,8582
+Tarpaulins,Africa,"Antsohihy, Madagascar",Madagascar,15
+Tarpaulins,Africa,"Benenitra, Madagascar",Madagascar,230
+Tarpaulins,Africa,"Farafangana, Madagascar",Madagascar,4
+Tarpaulins,Africa,"Fenerive Est, Madagascar",Madagascar,1548
+Tarpaulins,Africa,"Maevatanana, Madagascar",Madagascar,10
+Tarpaulins,Africa,"Mahajanga I, Madagascar",Madagascar,689
+Tarpaulins,Africa,"Maintirano, Madagascar",Madagascar,916
+Tarpaulins,Africa,"Manakara, Madagascar",Madagascar,511
+Tarpaulins,Africa,"Mananara, Madagascar",Madagascar,115
+Tarpaulins,Africa,"Miarinarivo, Madagascar",Madagascar,72
+Tarpaulins,Africa,"Morondava, Madagascar",Madagascar,50
+Tarpaulins,Africa,"Sainte-Marie, Madagascar",Madagascar,5
+Tarpaulins,Africa,"Soanierana Ivongo, Madagascar",Madagascar,450
+Tarpaulins,Africa,"Taolagnaro, Madagascar",Madagascar,200
+Tarpaulins,Africa,"Toamasina I, Madagascar",Madagascar,1200
+Tarpaulins,Africa,"Toliara, Madagascar",Madagascar,821
+Tents,Africa,"Ambovombe, Madagascar",Madagascar,7
+Tents,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,204
+Tents,Africa,"Fenerive Est, Madagascar",Madagascar,8
+Tents,Africa,"Mahajanga I, Madagascar",Madagascar,1
+Tents,Africa,"Maintirano, Madagascar",Madagascar,9
+Tents,Africa,"Manakara, Madagascar",Madagascar,13
+Tents,Africa,"Mananara, Madagascar",Madagascar,4
+Tents,Africa,"Maroantsetra, Madagascar",Madagascar,2
+Tents,Africa,"Taolagnaro, Madagascar",Madagascar,14
+Tents,Africa,"Toamasina I, Madagascar",Madagascar,17
+Tents,Africa,"Toliara, Madagascar",Madagascar,6
+WaterContainers,Africa,"Ambanja, Madagascar",Madagascar,375
+WaterContainers,Africa,"Ambatondrazaka, Madagascar",Madagascar,943
+WaterContainers,Africa,"Antalaha, Madagascar",Madagascar,1000
+WaterContainers,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,10100
+WaterContainers,Africa,"Antsohihy, Madagascar",Madagascar,2250
+WaterContainers,Africa,"Fenerive Est, Madagascar",Madagascar,2600
+WaterContainers,Africa,"Mahajanga I, Madagascar",Madagascar,150
+WaterContainers,Africa,"Manakara, Madagascar",Madagascar,3200
+WaterContainers,Africa,"Maroantsetra, Madagascar",Madagascar,595
+WaterContainers,Africa,"Midongy, Madagascar",Madagascar,3967
+WaterContainers,Africa,"Morondava, Madagascar",Madagascar,77
+WaterContainers,Africa,"Sainte-Marie, Madagascar",Madagascar,900
+WaterContainers,Africa,"Soanierana Ivongo, Madagascar",Madagascar,375
+WaterContainers,Africa,"Toamasina I, Madagascar",Madagascar,118
+WaterContainers,Africa,"Toliara, Madagascar",Madagascar,1200
+WaterContainers,Africa,"Vangaindrano, Madagascar",Madagascar,3476
diff --git a/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_75pct.csv b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_75pct.csv
new file mode 100644
index 0000000..c00f862
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_75pct.csv
@@ -0,0 +1,16 @@
+ItemName,Warehouse(Region),gglAddress,gglCountry,Total
+Blankets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,195507
+Buckets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,46922
+Clothes,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,117304
+HygieneAndDignityKits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,23461
+Kitchenset,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,23461
+Mosquitonets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,46922
+Otherlampslanterns,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,23461
+PersonalProtectionEquipmentkit(PPE),Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,117304
+SafeDeliverykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,1174
+SchoolPlaykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,2933
+ShelterToolKit,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,23461
+Sleepingmats,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,117304
+Tarpaulins,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,46922
+Tents,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,23461
+WaterContainers,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,46922
diff --git a/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_80pct.csv b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_80pct.csv
new file mode 100644
index 0000000..c9c0601
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_80pct.csv
@@ -0,0 +1,16 @@
+ItemName,Warehouse(Region),gglAddress,gglCountry,Total
+Blankets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,290012
+Buckets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,69603
+Clothes,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,174007
+HygieneAndDignityKits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,34802
+Kitchenset,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,34802
+Mosquitonets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,69603
+Otherlampslanterns,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,34802
+PersonalProtectionEquipmentkit(PPE),Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,174007
+SafeDeliverykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,1741
+SchoolPlaykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,4351
+ShelterToolKit,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,34802
+Sleepingmats,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,174007
+Tarpaulins,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,69603
+Tents,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,34802
+WaterContainers,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,69603
diff --git a/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_85pct.csv b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_85pct.csv
new file mode 100644
index 0000000..cfeb359
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_85pct.csv
@@ -0,0 +1,16 @@
+ItemName,Warehouse(Region),gglAddress,gglCountry,Total
+Blankets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,416785
+Buckets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,100029
+Clothes,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,250071
+HygieneAndDignityKits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,50015
+Kitchenset,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,50015
+Mosquitonets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,100029
+Otherlampslanterns,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,50015
+PersonalProtectionEquipmentkit(PPE),Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,250071
+SafeDeliverykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,2501
+SchoolPlaykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,6252
+ShelterToolKit,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,50015
+Sleepingmats,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,250071
+Tarpaulins,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,100029
+Tents,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,50015
+WaterContainers,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,100029
diff --git a/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_90pct.csv b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_90pct.csv
new file mode 100644
index 0000000..6479812
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_90pct.csv
@@ -0,0 +1,16 @@
+ItemName,Warehouse(Region),gglAddress,gglCountry,Total
+Blankets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,798672
+Buckets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,191682
+Clothes,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,479203
+HygieneAndDignityKits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,95841
+Kitchenset,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,95841
+Mosquitonets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,191682
+Otherlampslanterns,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,95841
+PersonalProtectionEquipmentkit(PPE),Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,479203
+SafeDeliverykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,4793
+SchoolPlaykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,11981
+ShelterToolKit,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,95841
+Sleepingmats,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,479203
+Tarpaulins,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,191682
+Tents,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,95841
+WaterContainers,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,191682
diff --git a/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_95pct.csv b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_95pct.csv
new file mode 100644
index 0000000..d30153c
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/inventory/actual_95pct.csv
@@ -0,0 +1,16 @@
+ItemName,Warehouse(Region),gglAddress,gglCountry,Total
+Blankets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,975018
+Buckets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,234005
+Clothes,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,585011
+HygieneAndDignityKits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,117003
+Kitchenset,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,117003
+Mosquitonets,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,234005
+Otherlampslanterns,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,117003
+PersonalProtectionEquipmentkit(PPE),Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,585011
+SafeDeliverykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,5851
+SchoolPlaykits,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,14626
+ShelterToolKit,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,117003
+Sleepingmats,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,585011
+Tarpaulins,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,234005
+Tents,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,117003
+WaterContainers,Africa,"Antananarivo Renivohitra, Madagascar",Madagascar,234005
diff --git a/optimization202/ESUPS_case_study/data/madagascar/items.csv b/optimization202/ESUPS_case_study/data/madagascar/items.csv
new file mode 100644
index 0000000..074fecc
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/items.csv
@@ -0,0 +1,16 @@
+ItemName,WeightMetricTon,CubicMeters
+Blankets,0.0015,0.01
+Buckets,0.00091,0.00617
+Clothes,0.0003,0.001
+HygieneAndDignityKits,0.00561,0.0262
+Kitchenset,0.00499,0.01763
+Mosquitonets,0.0004435,0.00135
+Otherlampslanterns,0.0002,0.00038
+PersonalProtectionEquipmentkit(PPE),0.0035,0.0000288
+SafeDeliverykits,0.245,1.036
+SchoolPlaykits,0.013,0.12
+ShelterToolKit,0.010421,0.02819
+Sleepingmats,0.00144,0.00745
+Tarpaulins,0.0041626,0.00943
+Tents,0.0528,0.19
+WaterContainers,0.0001738,0.0021
diff --git a/optimization202/ESUPS_case_study/data/madagascar/personsPerItem.csv b/optimization202/ESUPS_case_study/data/madagascar/personsPerItem.csv
new file mode 100644
index 0000000..4287115
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/personsPerItem.csv
@@ -0,0 +1,31 @@
+Item,Disaster Type,Month,gglCountry,PersonsPerItem
+Blankets,DEFAULT,DEFAULT,DEFAULT,0.6
+Buckets,DEFAULT,DEFAULT,DEFAULT,2.5
+Clothes,DEFAULT,DEFAULT,DEFAULT,1
+HygieneAndDignityKits,DEFAULT,DEFAULT,DEFAULT,5
+Kitchenset,DEFAULT,DEFAULT,DEFAULT,5
+Mosquitonets,DEFAULT,DEFAULT,DEFAULT,2.5
+Otherlampslanterns,DEFAULT,DEFAULT,DEFAULT,5
+PersonalProtectionEquipmentkit(PPE),DEFAULT,DEFAULT,DEFAULT,1
+SafeDeliverykits,DEFAULT,DEFAULT,DEFAULT,100
+SchoolPlaykits,DEFAULT,DEFAULT,DEFAULT,40
+ShelterToolKit,DEFAULT,DEFAULT,DEFAULT,5
+Sleepingmats,DEFAULT,DEFAULT,DEFAULT,1
+Tarpaulins,DEFAULT,DEFAULT,DEFAULT,2.5
+Tents,DEFAULT,DEFAULT,DEFAULT,5
+WaterContainers,DEFAULT,DEFAULT,DEFAULT,2.5
+Blankets,DEFAULT,-1,DEFAULT,0.6
+Buckets,DEFAULT,-1,DEFAULT,2.5
+Clothes,DEFAULT,-1,DEFAULT,1
+HygieneAndDignityKits,DEFAULT,-1,DEFAULT,5
+Kitchenset,DEFAULT,-1,DEFAULT,5
+Mosquitonets,DEFAULT,-1,DEFAULT,2.5
+Otherlampslanterns,DEFAULT,-1,DEFAULT,5
+PersonalProtectionEquipmentkit(PPE),DEFAULT,-1,DEFAULT,1
+SafeDeliverykits,DEFAULT,-1,DEFAULT,100
+SchoolPlaykits,DEFAULT,-1,DEFAULT,40
+ShelterToolKit,DEFAULT,-1,DEFAULT,5
+Sleepingmats,DEFAULT,-1,DEFAULT,1
+Tarpaulins,DEFAULT,-1,DEFAULT,2.5
+Tents,DEFAULT,-1,DEFAULT,5
+WaterContainers,DEFAULT,-1,DEFAULT,2.5
diff --git a/optimization202/ESUPS_case_study/data/madagascar/temp_items.csv b/optimization202/ESUPS_case_study/data/madagascar/temp_items.csv
new file mode 100644
index 0000000..074fecc
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/temp_items.csv
@@ -0,0 +1,16 @@
+ItemName,WeightMetricTon,CubicMeters
+Blankets,0.0015,0.01
+Buckets,0.00091,0.00617
+Clothes,0.0003,0.001
+HygieneAndDignityKits,0.00561,0.0262
+Kitchenset,0.00499,0.01763
+Mosquitonets,0.0004435,0.00135
+Otherlampslanterns,0.0002,0.00038
+PersonalProtectionEquipmentkit(PPE),0.0035,0.0000288
+SafeDeliverykits,0.245,1.036
+SchoolPlaykits,0.013,0.12
+ShelterToolKit,0.010421,0.02819
+Sleepingmats,0.00144,0.00745
+Tarpaulins,0.0041626,0.00943
+Tents,0.0528,0.19
+WaterContainers,0.0001738,0.0021
diff --git a/optimization202/ESUPS_case_study/data/madagascar/transportModes.csv b/optimization202/ESUPS_case_study/data/madagascar/transportModes.csv
new file mode 100644
index 0000000..35c098e
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/transportModes.csv
@@ -0,0 +1,3 @@
+Mode,DistanceMethod,BigMCostElim,MaxDrivingTimeCutAboveHrs
+Truck,google,1e9,100
+Air,crowScale,1e9,100
diff --git a/optimization202/ESUPS_case_study/data/madagascar/transportParameters.csv b/optimization202/ESUPS_case_study/data/madagascar/transportParameters.csv
new file mode 100644
index 0000000..158e0b6
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/madagascar/transportParameters.csv
@@ -0,0 +1,12 @@
+Attribute,Mode,gglAddress,Number
+FixedAddlTime_Hrs,Truck,DEFAULT,24
+FixedAddlTime_Hrs,Air,DEFAULT,100000000
+StretchTimeFactor,Truck,DEFAULT,1.1
+StretchTimeFactor,Air,DEFAULT,100000000
+SpeedKmPerHr,Air,DEFAULT,0.000001
+FixedAddlCost_USD,Truck,DEFAULT,233000
+FixedAddlCost_USD,Air,DEFAULT,1.00E+08
+VarCost_USD_ton_km,Truck,DEFAULT,1115
+VarCost_USD_ton_km,Air,DEFAULT,1.00E+08
+StretchDistanceFactor,Truck,DEFAULT,1
+StretchDistanceFactor,Air,DEFAULT,1
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/abilityToRespond.csv b/optimization202/ESUPS_case_study/data/vanuatu/abilityToRespond.csv
new file mode 100644
index 0000000..958db29
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/abilityToRespond.csv
@@ -0,0 +1,216 @@
+gglCountry,item,capacityToRespond
+Afghanistan,DEFAULT,10
+Albania,DEFAULT,10
+Algeria,DEFAULT,10
+American Samoa,DEFAULT,10
+Angola,DEFAULT,10
+Anguilla,DEFAULT,10
+Antigua and Barbuda,DEFAULT,10
+Argentina,DEFAULT,10
+Armenia,DEFAULT,10
+Australia,DEFAULT,2.00E+15
+Austria,DEFAULT,2.00E+15
+Azerbaijan,DEFAULT,10
+Bahrain,DEFAULT,10
+Bangladesh,DEFAULT,10
+Barbados,DEFAULT,10
+Belarus,DEFAULT,10
+Belgium,DEFAULT,2.00E+15
+Belize,DEFAULT,10
+Benin,DEFAULT,10
+Bermuda,DEFAULT,10
+Bhutan,DEFAULT,10
+Bolivia,DEFAULT,10
+Bosnia and Herzegovina,DEFAULT,10
+Botswana,DEFAULT,10
+Brazil,DEFAULT,10
+British Virgin Islands,DEFAULT,10
+Brunei,DEFAULT,10
+Bulgaria,DEFAULT,2.00E+15
+Burkina Faso,DEFAULT,10
+Burundi,DEFAULT,10
+Cambodia,DEFAULT,10
+Cameroon,DEFAULT,10
+Canada,DEFAULT,2.00E+15
+Cape Verde,DEFAULT,10
+Cayman Islands,DEFAULT,10
+Central African Republic,DEFAULT,10
+Chad,DEFAULT,10
+Chile,DEFAULT,10
+China,DEFAULT,10
+Colombia,DEFAULT,10
+Comoros,DEFAULT,10
+Congo,DEFAULT,10
+Cook Islands,DEFAULT,10
+Costa Rica,DEFAULT,10
+Cote d'Ivoire,DEFAULT,10
+Croatia,DEFAULT,2.00E+15
+Cuba,DEFAULT,10
+Curacao,DEFAULT,10
+Czech Republic,DEFAULT,2.00E+15
+Democratic Republic of the Congo,DEFAULT,10
+Denmark,DEFAULT,2.00E+15
+Djibouti,DEFAULT,10
+Dominica,DEFAULT,10
+Dominican Republic,DEFAULT,10
+Ecuador,DEFAULT,10
+Egypt,DEFAULT,10
+El Salvador,DEFAULT,10
+Equatorial Guinea,DEFAULT,10
+Eritrea,DEFAULT,10
+Estonia,DEFAULT,2.00E+15
+Ethiopia,DEFAULT,10
+Fiji,DEFAULT,10
+Finland,DEFAULT,2.00E+15
+France,DEFAULT,2.00E+15
+French Guiana,DEFAULT,10
+French Polynesia,DEFAULT,10
+Gabon,DEFAULT,10
+Georgia,DEFAULT,10
+Germany,DEFAULT,2.00E+15
+Ghana,DEFAULT,10
+Greece,DEFAULT,2.00E+15
+Grenada,DEFAULT,10
+Guadeloupe,DEFAULT,10
+Guam,DEFAULT,10
+Guatemala,DEFAULT,10
+Guinea,DEFAULT,10
+Guinea-Bissau,DEFAULT,10
+Guyana,DEFAULT,10
+Haiti,DEFAULT,10
+Honduras,DEFAULT,10
+Hong Kong,DEFAULT,10
+Hungary,DEFAULT,2.00E+15
+Iceland,DEFAULT,2.00E+15
+India,DEFAULT,10
+Indonesia,DEFAULT,10
+Iran,DEFAULT,10
+Iraq,DEFAULT,10
+Ireland,DEFAULT,2.00E+15
+Israel,DEFAULT,10
+Italy,DEFAULT,2.00E+15
+Jamaica,DEFAULT,10
+Japan,DEFAULT,10
+Jordan,DEFAULT,10
+Kazakhstan,DEFAULT,10
+Kenya,DEFAULT,10
+Kiribati,DEFAULT,10
+Kuwait,DEFAULT,10
+Kyrgyzstan,DEFAULT,10
+Laos,DEFAULT,10
+Latvia,DEFAULT,2.00E+15
+Lebanon,DEFAULT,10
+Lesotho,DEFAULT,10
+Liberia,DEFAULT,10
+Libya,DEFAULT,10
+Lithuania,DEFAULT,2.00E+15
+Luxembourg,DEFAULT,2.00E+15
+Macau,DEFAULT,10
+Macedonia (FYROM),DEFAULT,10
+Madagascar,DEFAULT,10
+Malawi,DEFAULT,10
+Malaysia,DEFAULT,10
+Maldives,DEFAULT,10
+Mali,DEFAULT,10
+Malta,DEFAULT,2.00E+15
+Marshall Islands,DEFAULT,10
+Martinique,DEFAULT,10
+Mauritania,DEFAULT,10
+Mauritius,DEFAULT,10
+Mayotte,DEFAULT,10
+Mexico,DEFAULT,10
+Micronesia,DEFAULT,10
+Moldova,DEFAULT,10
+Mongolia,DEFAULT,10
+Montenegro,DEFAULT,10
+Montserrat,DEFAULT,10
+Morocco,DEFAULT,10
+Mozambique,DEFAULT,10
+Namibia,DEFAULT,10
+Nepal,DEFAULT,10
+New Caledonia,DEFAULT,10
+New Zealand,DEFAULT,2.00E+15
+Nicaragua,DEFAULT,10
+Nicosia,DEFAULT,2.00E+15
+Niger,DEFAULT,10
+Nigeria,DEFAULT,10
+Niue,DEFAULT,10
+North Korea,DEFAULT,10
+Northern Mariana Islands,DEFAULT,10
+Norway,DEFAULT,2.00E+15
+Oman,DEFAULT,10
+Pakistan,DEFAULT,10
+Palau,DEFAULT,10
+Panama,DEFAULT,10
+Papua New Guinea,DEFAULT,10
+Paraguay,DEFAULT,10
+Peru,DEFAULT,10
+Philippines,DEFAULT,10
+Poland,DEFAULT,2.00E+15
+Portugal,DEFAULT,2.00E+15
+Puerto Rico,DEFAULT,10
+Qatar,DEFAULT,10
+Republic of the Union of Myanmar,DEFAULT,10
+Reunion,DEFAULT,10
+Romania,DEFAULT,2.00E+15
+Russia,DEFAULT,10
+Rwanda,DEFAULT,10
+Saint Helena,DEFAULT,10
+Saint Kitts and Nevis,DEFAULT,10
+Saint Lucia,DEFAULT,10
+Saint Vincent and the Grenadines,DEFAULT,10
+Samoa,DEFAULT,10
+Sao Tome and Principe,DEFAULT,10
+Saudi Arabia,DEFAULT,10
+Senegal,DEFAULT,10
+Serbia,DEFAULT,10
+Seychelles,DEFAULT,10
+Sierra Leone,DEFAULT,10
+Singapore,DEFAULT,10
+Slovakia,DEFAULT,2.00E+15
+Slovenia,DEFAULT,2.00E+15
+Solomon Islands,DEFAULT,10
+Somalia,DEFAULT,10
+South Africa,DEFAULT,10
+South Korea,DEFAULT,10
+South Sudan,DEFAULT,10
+Spain,DEFAULT,2.00E+15
+Sri Lanka,DEFAULT,10
+Sudan,DEFAULT,10
+Suriname,DEFAULT,10
+Swaziland,DEFAULT,10
+Sweden,DEFAULT,2.00E+15
+Switzerland,DEFAULT,2.00E+15
+Syria,DEFAULT,10
+Taiwan,DEFAULT,10
+Tajikistan,DEFAULT,10
+Tanzania,DEFAULT,10
+Thailand,DEFAULT,10
+The Bahamas,DEFAULT,10
+The Gambia,DEFAULT,10
+The Netherlands,DEFAULT,2.00E+15
+Timor-Leste,DEFAULT,10
+Togo,DEFAULT,10
+Tokelau,DEFAULT,10
+Tonga,DEFAULT,10
+Trinidad and Tobago,DEFAULT,10
+Tunisia,DEFAULT,10
+Turkey,DEFAULT,10
+Turkmenistan,DEFAULT,10
+Turks and Caicos Islands,DEFAULT,10
+Tuvalu,DEFAULT,10
+U.S. Virgin Islands,DEFAULT,10
+Uganda,DEFAULT,10
+Ukraine,DEFAULT,10
+United Arab Emirates,DEFAULT,10
+United Kingdom,DEFAULT,2.00E+15
+United States,DEFAULT,2.00E+15
+Uruguay,DEFAULT,10
+Uzbekistan,DEFAULT,10
+Vanuatu,DEFAULT,10
+Venezuela,DEFAULT,10
+Vietnam,DEFAULT,10
+Wallis and Futuna,DEFAULT,10
+Yemen,DEFAULT,10
+Zambia,DEFAULT,10
+Zimbabwe,DEFAULT,10
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/countryContinents.csv b/optimization202/ESUPS_case_study/data/vanuatu/countryContinents.csv
new file mode 100644
index 0000000..43921a4
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/countryContinents.csv
@@ -0,0 +1,216 @@
+gglCountry,Continent
+Central African Republic,Africa
+Sao Tome and Principe,Africa
+Cote d'Ivoire,Africa
+Libya,Africa
+Democratic Republic of the Congo,Africa
+The Gambia,Africa
+Guinea-Bissau,Africa
+Cape Verde,Africa
+Reunion,Africa
+Saint Helena,Africa
+Mayotte,Africa
+Tanzania,Africa
+South Sudan,Africa
+Congo,Africa
+Algeria,Africa
+Angola,Africa
+Benin,Africa
+Botswana,Africa
+Burkina Faso,Africa
+Burundi,Africa
+Cameroon,Africa
+Chad,Africa
+Comoros,Africa
+Djibouti,Africa
+Egypt,Africa
+Equatorial Guinea,Africa
+Eritrea,Africa
+Ethiopia,Africa
+Gabon,Africa
+Ghana,Africa
+Guinea,Africa
+Kenya,Africa
+Lesotho,Africa
+Liberia,Africa
+Madagascar,Africa
+Malawi,Africa
+Mali,Africa
+Mauritania,Africa
+Mauritius,Africa
+Morocco,Africa
+Mozambique,Africa
+Namibia,Africa
+Niger,Africa
+Nigeria,Africa
+Rwanda,Africa
+Senegal,Africa
+Seychelles,Africa
+Sierra Leone,Africa
+Somalia,Africa
+South Africa,Africa
+Sudan,Africa
+Swaziland,Africa
+Togo,Africa
+Tunisia,Africa
+Uganda,Africa
+Zambia,Africa
+Zimbabwe,Africa
+Taiwan,Asia
+Macau,Asia
+North Korea,Asia
+Vietnam,Asia
+Timor-Leste,Asia
+Syria,Asia
+Brunei,Asia
+Iran,Asia
+South Korea,Asia
+Israel,Asia
+Yemen,Asia
+Hong Kong,Asia
+Laos,Asia
+China,Asia
+Russia,Asia
+Afghanistan,Asia
+Bahrain,Asia
+Bangladesh,Asia
+Bhutan,Asia
+Cambodia,Asia
+India,Asia
+Indonesia,Asia
+Iraq,Asia
+Japan,Asia
+Jordan,Asia
+Kazakhstan,Asia
+Kuwait,Asia
+Kyrgyzstan,Asia
+Lebanon,Asia
+Malaysia,Asia
+Maldives,Asia
+Mongolia,Asia
+Republic of the Union of Myanmar,Asia
+Nepal,Asia
+Oman,Asia
+Pakistan,Asia
+Philippines,Asia
+Qatar,Asia
+Saudi Arabia,Asia
+Singapore,Asia
+Sri Lanka,Asia
+Tajikistan,Asia
+Thailand,Asia
+Turkey,Asia
+Turkmenistan,Asia
+United Arab Emirates,Asia
+Uzbekistan,Asia
+Germany,Europe
+Portugal,Europe
+Czech Republic,Europe
+Moldova,Europe
+Macedonia (FYROM),Europe
+Bosnia and Herzegovina,Europe
+Spain,Europe
+United Kingdom,Europe
+Serbia,Europe
+Albania,Europe
+Armenia,Europe
+Austria,Europe
+Azerbaijan,Europe
+Belarus,Europe
+Belgium,Europe
+Bulgaria,Europe
+Croatia,Europe
+Nicosia,Europe
+Denmark,Europe
+Estonia,Europe
+Finland,Europe
+France,Europe
+Georgia,Europe
+Greece,Europe
+Hungary,Europe
+Iceland,Europe
+Ireland,Europe
+Italy,Europe
+Latvia,Europe
+Lithuania,Europe
+Luxembourg,Europe
+Malta,Europe
+Montenegro,Europe
+The Netherlands,Europe
+Norway,Europe
+Poland,Europe
+Romania,Europe
+Slovakia,Europe
+Slovenia,Europe
+Sweden,Europe
+Switzerland,Europe
+Ukraine,Europe
+Saint Vincent and the Grenadines,NorthAmerica
+British Virgin Islands,NorthAmerica
+Dominican Republic,NorthAmerica
+U.S. Virgin Islands,NorthAmerica
+Curacao,NorthAmerica
+Turks and Caicos Islands,NorthAmerica
+Saint Kitts and Nevis,NorthAmerica
+Martinique,NorthAmerica
+Saint Lucia,NorthAmerica
+Guadeloupe,NorthAmerica
+Anguilla,NorthAmerica
+Antigua and Barbuda,NorthAmerica
+The Bahamas,NorthAmerica
+Barbados,NorthAmerica
+Belize,NorthAmerica
+Bermuda,NorthAmerica
+Canada,NorthAmerica
+Cayman Islands,NorthAmerica
+Costa Rica,NorthAmerica
+Cuba,NorthAmerica
+Dominica,NorthAmerica
+El Salvador,NorthAmerica
+Grenada,NorthAmerica
+Guatemala,NorthAmerica
+Haiti,NorthAmerica
+Honduras,NorthAmerica
+Jamaica,NorthAmerica
+Mexico,NorthAmerica
+Montserrat,NorthAmerica
+Nicaragua,NorthAmerica
+Panama,NorthAmerica
+Puerto Rico,NorthAmerica
+Trinidad and Tobago,NorthAmerica
+United States,NorthAmerica
+Wallis and Futuna,Oceania
+Marshall Islands,Oceania
+Cook Islands,Oceania
+Northern Mariana Islands,Oceania
+Tokelau,Oceania
+Solomon Islands,Oceania
+Micronesia,Oceania
+American Samoa,Oceania
+Australia,Oceania
+Fiji,Oceania
+French Polynesia,Oceania
+Guam,Oceania
+Kiribati,Oceania
+New Caledonia,Oceania
+New Zealand,Oceania
+Niue,Oceania
+Palau,Oceania
+Papua New Guinea,Oceania
+Samoa,Oceania
+Tonga,Oceania
+Tuvalu,Oceania
+Vanuatu,Oceania
+Argentina,SouthAmerica
+Bolivia,SouthAmerica
+Brazil,SouthAmerica
+Chile,SouthAmerica
+Colombia,SouthAmerica
+Ecuador,SouthAmerica
+French Guiana,SouthAmerica
+Guyana,SouthAmerica
+Paraguay,SouthAmerica
+Peru,SouthAmerica
+Suriname,SouthAmerica
+Uruguay,SouthAmerica
+Venezuela,SouthAmerica
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/depotCoordinates.csv b/optimization202/ESUPS_case_study/data/vanuatu/depotCoordinates.csv
new file mode 100644
index 0000000..bf93432
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/depotCoordinates.csv
@@ -0,0 +1,7 @@
+depotCity,gglLat,gglLong,gglCountryAscii,gglAddressAscii
+Torba,-13.873643999999956,167.5495830000001,Vanuatu,"Torba, Vanuatu"
+Sanma,-15.513757933999957,167.1799922040001,Vanuatu,"Sanma, Vanuatu"
+Penama,-15.548909039999955,168.1484143,Vanuatu,"Penama, Vanuatu"
+Malampa,-16.10622799099997,167.41678100600006,Vanuatu,"Malampa, Vanuatu"
+Shefa,-17.73763299799998,168.3157348850001,Vanuatu,"Shefa, Vanuatu"
+Tafea,-19.54447261799993,169.2801722150001,Vanuatu,"Tafea, Vanuatu"
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/depotMapping.csv b/optimization202/ESUPS_case_study/data/vanuatu/depotMapping.csv
new file mode 100644
index 0000000..63bcef6
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/depotMapping.csv
@@ -0,0 +1,7 @@
+gglAddressAsciiMapFrom,gglAddressAsciiMapTo
+"Torba, Vanuatu","Torba, Vanuatu"
+"Sanma, Vanuatu","Sanma, Vanuatu"
+"Penama, Vanuatu","Penama, Vanuatu"
+"Malampa, Vanuatu","Malampa, Vanuatu"
+"Shefa, Vanuatu","Shefa, Vanuatu"
+"Tafea, Vanuatu","Tafea, Vanuatu"
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/depotSelection.csv b/optimization202/ESUPS_case_study/data/vanuatu/depotSelection.csv
new file mode 100644
index 0000000..7cb5bda
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/depotSelection.csv
@@ -0,0 +1,7 @@
+gglAddressAscii,gglCountry,include
+"Torba, Vanuatu",Vanuatu,1
+"Sanma, Vanuatu",Vanuatu,1
+"Penama, Vanuatu",Vanuatu,1
+"Malampa, Vanuatu",Vanuatu,1
+"Shefa, Vanuatu",Vanuatu,1
+"Tafea, Vanuatu",Vanuatu,1
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/disasterCoordinates.csv b/optimization202/ESUPS_case_study/data/vanuatu/disasterCoordinates.csv
new file mode 100644
index 0000000..cad4a3f
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/disasterCoordinates.csv
@@ -0,0 +1,7 @@
+emdatCity,gglLat,gglLong,gglCountryAscii,gglAddressAscii
+Torba,-13.873643999999956,167.5495830000001,Vanuatu,"Torba, Vanuatu"
+Sanma,-15.513757933999957,167.1799922040001,Vanuatu,"Sanma, Vanuatu"
+Penama,-15.548909039999955,168.1484143,Vanuatu,"Penama, Vanuatu"
+Malampa,-16.10622799099997,167.41678100600006,Vanuatu,"Malampa, Vanuatu"
+Shefa,-17.73763299799998,168.3157348850001,Vanuatu,"Shefa, Vanuatu"
+Tafea,-19.54447261799993,169.2801722150001,Vanuatu,"Tafea, Vanuatu"
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/disasterTypeSelection.csv b/optimization202/ESUPS_case_study/data/vanuatu/disasterTypeSelection.csv
new file mode 100644
index 0000000..b1e235a
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/disasterTypeSelection.csv
@@ -0,0 +1,22 @@
+disasterType,include
+Complex Disasters,
+Drought,
+Earthquake (seismic activity),1
+Earthquake,1
+Epidemic,1
+Extreme temperature,
+Flood,1
+Industrial Accident,
+Insect Infestation,
+Insect infestation,
+Landslide,1
+Mass movement dry,1
+Mass Movement Dry,1
+Mass Movement Wet,1
+Mass movement wet,1
+Miscellaneous accident,
+Storm,1
+Transport accident,
+Transport Accident,
+Volcano,1
+Wildfire,1
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/disasters.csv b/optimization202/ESUPS_case_study/data/vanuatu/disasters.csv
new file mode 100644
index 0000000..8061f64
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/disasters.csv
@@ -0,0 +1,64 @@
+Day,Month,Year,origDistrict,Type,DisasterID,TotAffected,gglCountry,gglAddress
+16.0,1.0,1985,Penama,Storm,1985-0020-VUT,28870.0,Vanuatu,"Penama, Vanuatu"
+16.0,1.0,1985,Sanma,Storm,1985-0020-VUT,42148.0,Vanuatu,"Sanma, Vanuatu"
+7.0,2.0,1987,Shefa,Storm,1987-0057-VUT,33969.0,Vanuatu,"Shefa, Vanuatu"
+7.0,2.0,1987,Tafea,Storm,1987-0057-VUT,14031.0,Vanuatu,"Tafea, Vanuatu"
+11.0,1.0,1988,Sanma,Storm,1988-0040-VUT,3903.0,Vanuatu,"Sanma, Vanuatu"
+11.0,1.0,1988,Torba,Storm,1988-0040-VUT,797.0,Vanuatu,"Torba, Vanuatu"
+,8.0,1988,Malampa,Storm,1988-0676-VUT,636.0,Vanuatu,"Malampa, Vanuatu"
+,8.0,1988,Shefa,Storm,1988-0676-VUT,1364.0,Vanuatu,"Shefa, Vanuatu"
+9.0,3.0,1992,Shefa,Storm,1992-0050-VUT,1150.0,Vanuatu,"Shefa, Vanuatu"
+30.0,3.0,1993,Shefa,Storm,1993-0024-VUT,12005.0,Vanuatu,"Shefa, Vanuatu"
+21.0,3.0,1998,Sanma,Storm,1998-0090-VUT,1254.0,Vanuatu,"Sanma, Vanuatu"
+21.0,3.0,1998,Tafea,Storm,1998-0090-VUT,890.0,Vanuatu,"Tafea, Vanuatu"
+21.0,3.0,1998,Torba,Storm,1998-0090-VUT,256.0,Vanuatu,"Torba, Vanuatu"
+27.0,11.0,1999,Malampa,Earthquake,1999-0524-VUT,3526.0,Vanuatu,"Malampa, Vanuatu"
+27.0,11.0,1999,Penama,Earthquake,1999-0524-VUT,3016.0,Vanuatu,"Penama, Vanuatu"
+27.0,11.0,1999,Shefa,Earthquake,1999-0524-VUT,7558.0,Vanuatu,"Shefa, Vanuatu"
+7.0,4.0,2001,Malampa,Storm,2001-0143-VUT,130.0,Vanuatu,"Malampa, Vanuatu"
+7.0,4.0,2001,Penama,Storm,2001-0143-VUT,112.0,Vanuatu,"Penama, Vanuatu"
+7.0,4.0,2001,Sanma,Storm,2001-0143-VUT,163.0,Vanuatu,"Sanma, Vanuatu"
+7.0,4.0,2001,Shefa,Storm,2001-0143-VUT,280.0,Vanuatu,"Shefa, Vanuatu"
+7.0,4.0,2001,Tafea,Storm,2001-0143-VUT,115.0,Vanuatu,"Tafea, Vanuatu"
+8.0,6.0,2001,Malampa,Volcanic activity,2001-0254-VUT,1627.0,Vanuatu,"Malampa, Vanuatu"
+3.0,1.0,2002,Shefa,Earthquake,2002-0002-VUT,500.0,Vanuatu,"Shefa, Vanuatu"
+27.0,11.0,2002,Torba,Earthquake,2002-0770-VUT,503.0,Vanuatu,"Torba, Vanuatu"
+21.0,12.0,2002,Tafea,Flood,2002-0829-VUT,3001.0,Vanuatu,"Tafea, Vanuatu"
+25.0,2.0,2004,Malampa,Storm,2004-0080-VUT,11057.0,Vanuatu,"Malampa, Vanuatu"
+25.0,2.0,2004,Penama,Storm,2004-0080-VUT,9458.0,Vanuatu,"Penama, Vanuatu"
+25.0,2.0,2004,Shefa,Storm,2004-0080-VUT,23703.0,Vanuatu,"Shefa, Vanuatu"
+25.0,2.0,2004,Tafea,Storm,2004-0080-VUT,9791.0,Vanuatu,"Tafea, Vanuatu"
+27.0,11.0,2005,Penama,Volcanic activity,2005-0664-VUT,5000.0,Vanuatu,"Penama, Vanuatu"
+9.0,5.0,2006,Malampa,Volcanic activity,2006-0281-VUT,1572.0,Vanuatu,"Malampa, Vanuatu"
+,12.0,2008,Malampa,Volcanic activity,2008-0659-VUT,7275.0,Vanuatu,"Malampa, Vanuatu"
+15.0,4.0,2009,Malampa,Flood,2009-0171-VUT,950.0,Vanuatu,"Malampa, Vanuatu"
+26.0,11.0,2009,Torba,Volcanic activity,2009-0553-VUT,400.0,Vanuatu,"Torba, Vanuatu"
+12.0,1.0,2011,Tafea,Storm,2011-0071-VUT,32000.0,Vanuatu,"Tafea, Vanuatu"
+9.0,3.0,2014,Malampa,Storm,2014-0096-VUT,3636.0,Vanuatu,"Malampa, Vanuatu"
+9.0,3.0,2014,Penama,Storm,2014-0096-VUT,3110.0,Vanuatu,"Penama, Vanuatu"
+9.0,3.0,2014,Sanma,Storm,2014-0096-VUT,4540.0,Vanuatu,"Sanma, Vanuatu"
+9.0,3.0,2014,Shefa,Storm,2014-0096-VUT,7794.0,Vanuatu,"Shefa, Vanuatu"
+9.0,3.0,2014,Torba,Storm,2014-0096-VUT,927.0,Vanuatu,"Torba, Vanuatu"
+12.0,3.0,2015,Malampa,Storm,2015-0093-VUT,29428.0,Vanuatu,"Malampa, Vanuatu"
+12.0,3.0,2015,Penama,Storm,2015-0093-VUT,25173.0,Vanuatu,"Penama, Vanuatu"
+12.0,3.0,2015,Sanma,Storm,2015-0093-VUT,36751.0,Vanuatu,"Sanma, Vanuatu"
+12.0,3.0,2015,Shefa,Storm,2015-0093-VUT,63087.0,Vanuatu,"Shefa, Vanuatu"
+12.0,3.0,2015,Tafea,Storm,2015-0093-VUT,26059.0,Vanuatu,"Tafea, Vanuatu"
+12.0,3.0,2015,Torba,Storm,2015-0093-VUT,7500.0,Vanuatu,"Torba, Vanuatu"
+30.0,11.0,2016,Malampa,Epidemic,2016-0528-VUT,63.0,Vanuatu,"Malampa, Vanuatu"
+30.0,11.0,2016,Sanma,Epidemic,2016-0528-VUT,79.0,Vanuatu,"Sanma, Vanuatu"
+30.0,11.0,2016,Shefa,Epidemic,2016-0528-VUT,136.0,Vanuatu,"Shefa, Vanuatu"
+30.0,11.0,2016,Tafea,Epidemic,2016-0528-VUT,56.0,Vanuatu,"Tafea, Vanuatu"
+30.0,11.0,2016,Torba,Epidemic,2016-0528-VUT,16.0,Vanuatu,"Torba, Vanuatu"
+7.0,5.0,2017,Malampa,Storm,2017-0231-VUT,1024.0,Vanuatu,"Malampa, Vanuatu"
+7.0,5.0,2017,Sanma,Storm,2017-0231-VUT,1279.0,Vanuatu,"Sanma, Vanuatu"
+7.0,5.0,2017,Torba,Storm,2017-0231-VUT,261.0,Vanuatu,"Torba, Vanuatu"
+23.0,9.0,2017,Penama,Volcanic activity,2017-0394-VUT,11000.0,Vanuatu,"Penama, Vanuatu"
+,3.0,2018,Penama,Volcanic activity,2018-0147-VUT,6300.0,Vanuatu,"Penama, Vanuatu"
+15.0,12.0,2018,Malampa,Volcanic activity,2018-0456-VUT,7286.0,Vanuatu,"Malampa, Vanuatu"
+6.0,4.0,2020,Malampa,Storm,2020-0132-VUT,23646.0,Vanuatu,"Malampa, Vanuatu"
+6.0,4.0,2020,Penama,Storm,2020-0132-VUT,20227.0,Vanuatu,"Penama, Vanuatu"
+6.0,4.0,2020,Sanma,Storm,2020-0132-VUT,29530.0,Vanuatu,"Sanma, Vanuatu"
+6.0,4.0,2020,Shefa,Storm,2020-0132-VUT,50691.0,Vanuatu,"Shefa, Vanuatu"
+6.0,4.0,2020,Torba,Storm,2020-0132-VUT,6026.0,Vanuatu,"Torba, Vanuatu"
+21.0,10.0,2021,Tafea,Volcanic activity,2021-0847-VUT,3586.0,Vanuatu,"Tafea, Vanuatu"
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/distanceMatrix.csv b/optimization202/ESUPS_case_study/data/vanuatu/distanceMatrix.csv
new file mode 100644
index 0000000..df72334
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/distanceMatrix.csv
@@ -0,0 +1,37 @@
+emdatCity,depotCity,distance_km,drivingTime_hrs,depotCountry,emdatCountry,gglCountryAscii_depot,gglCountryAscii_disaster,depotGglAddressAscii,disasterGglAddressAscii,depotGglLat,depotGglLong,disasterGglLat,disasterGglLong
+Torba,Torba,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Torba, Vanuatu",-13.873643999999956,167.5495830000001,-13.873643999999956,167.5495830000001
+Torba,Sanma,184.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Torba, Vanuatu",-15.513757933999955,167.1799922040001,-13.873643999999956,167.5495830000001
+Torba,Penama,195.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Torba, Vanuatu",-15.548909039999955,168.1484143,-13.873643999999956,167.5495830000001
+Torba,Malampa,241.0,16.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Torba, Vanuatu",-16.10622799099997,167.41678100600006,-13.873643999999956,167.5495830000001
+Torba,Shefa,430.0,26.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Torba, Vanuatu",-17.73763299799998,168.3157348850001,-13.873643999999956,167.5495830000001
+Torba,Tafea,657.0,36.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Torba, Vanuatu",-19.54447261799993,169.2801722150001,-13.873643999999956,167.5495830000001
+Sanma,Torba,184.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Sanma, Vanuatu",-13.873643999999956,167.5495830000001,-15.513757933999955,167.1799922040001
+Sanma,Sanma,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Sanma, Vanuatu",-15.513757933999955,167.1799922040001,-15.513757933999955,167.1799922040001
+Sanma,Penama,103.0,9.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Sanma, Vanuatu",-15.548909039999955,168.1484143,-15.513757933999955,167.1799922040001
+Sanma,Malampa,68.0,4.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Sanma, Vanuatu",-16.10622799099997,167.41678100600006,-15.513757933999955,167.1799922040001
+Sanma,Shefa,274.0,16.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Sanma, Vanuatu",-17.73763299799998,168.3157348850001,-15.513757933999955,167.1799922040001
+Sanma,Tafea,499.0,28.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Sanma, Vanuatu",-19.54447261799993,169.2801722150001,-15.513757933999955,167.1799922040001
+Penama,Torba,195.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Penama, Vanuatu",-13.873643999999956,167.5495830000001,-15.548909039999955,168.1484143
+Penama,Sanma,103.0,9.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Penama, Vanuatu",-15.513757933999955,167.1799922040001,-15.548909039999955,168.1484143
+Penama,Penama,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Penama, Vanuatu",-15.548909039999955,168.1484143,-15.548909039999955,168.1484143
+Penama,Malampa,97.0,6.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Penama, Vanuatu",-16.10622799099997,167.41678100600006,-15.548909039999955,168.1484143
+Penama,Shefa,244.0,14.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Penama, Vanuatu",-17.73763299799998,168.3157348850001,-15.548909039999955,168.1484143
+Penama,Tafea,461.0,26.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Penama, Vanuatu",-19.54447261799993,169.2801722150001,-15.548909039999955,168.1484143
+Malampa,Torba,241.0,16.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Malampa, Vanuatu",-13.873643999999956,167.5495830000001,-16.10622799099997,167.41678100600006
+Malampa,Sanma,68.0,4.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Malampa, Vanuatu",-15.513757933999955,167.1799922040001,-16.10622799099997,167.41678100600006
+Malampa,Penama,97.0,6.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Malampa, Vanuatu",-15.548909039999955,168.1484143,-16.10622799099997,167.41678100600006
+Malampa,Malampa,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Malampa, Vanuatu",-16.10622799099997,167.41678100600006,-16.10622799099997,167.41678100600006
+Malampa,Shefa,205.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Malampa, Vanuatu",-17.73763299799998,168.3157348850001,-16.10622799099997,167.41678100600006
+Malampa,Tafea,431.0,24.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Malampa, Vanuatu",-19.54447261799993,169.2801722150001,-16.10622799099997,167.41678100600006
+Shefa,Torba,430.0,26.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Shefa, Vanuatu",-13.873643999999956,167.5495830000001,-17.73763299799998,168.3157348850001
+Shefa,Sanma,274.0,16.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Shefa, Vanuatu",-15.513757933999955,167.1799922040001,-17.73763299799998,168.3157348850001
+Shefa,Penama,244.0,14.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Shefa, Vanuatu",-15.548909039999955,168.1484143,-17.73763299799998,168.3157348850001
+Shefa,Malampa,205.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Shefa, Vanuatu",-16.10622799099997,167.41678100600006,-17.73763299799998,168.3157348850001
+Shefa,Shefa,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Shefa, Vanuatu",-17.73763299799998,168.3157348850001,-17.73763299799998,168.3157348850001
+Shefa,Tafea,227.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Shefa, Vanuatu",-19.54447261799993,169.2801722150001,-17.73763299799998,168.3157348850001
+Tafea,Torba,657.0,36.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Tafea, Vanuatu",-13.873643999999956,167.5495830000001,-19.54447261799993,169.2801722150001
+Tafea,Sanma,499.0,28.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Tafea, Vanuatu",-15.513757933999955,167.1799922040001,-19.54447261799993,169.2801722150001
+Tafea,Penama,461.0,26.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Tafea, Vanuatu",-15.548909039999955,168.1484143,-19.54447261799993,169.2801722150001
+Tafea,Malampa,431.0,24.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Tafea, Vanuatu",-16.10622799099997,167.41678100600006,-19.54447261799993,169.2801722150001
+Tafea,Shefa,227.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Tafea, Vanuatu",-17.73763299799998,168.3157348850001,-19.54447261799993,169.2801722150001
+Tafea,Tafea,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Tafea, Vanuatu",-19.54447261799993,169.2801722150001,-19.54447261799993,169.2801722150001
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual.csv
new file mode 100644
index 0000000..a39de43
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual.csv
@@ -0,0 +1,56 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Malampa, Vanuatu",Vanuatu,260
+Hygiene Kits,Oceania,"Malampa, Vanuatu",Vanuatu,64
+Kitchen sets,Oceania,"Malampa, Vanuatu",Vanuatu,118
+Shelter tool kits,Oceania,"Malampa, Vanuatu",Vanuatu,93
+Sleeping mats,Oceania,"Malampa, Vanuatu",Vanuatu,280
+Tarpaulins,Oceania,"Malampa, Vanuatu",Vanuatu,415
+Water Containers,Oceania,"Malampa, Vanuatu",Vanuatu,225
+mosquito nets,Oceania,"Malampa, Vanuatu",Vanuatu,400
+Blankets,Oceania,"Penama, Vanuatu",Vanuatu,160
+Hygiene Kits,Oceania,"Penama, Vanuatu",Vanuatu,140
+Kitchen sets,Oceania,"Penama, Vanuatu",Vanuatu,180
+Shelter tool kits,Oceania,"Penama, Vanuatu",Vanuatu,170
+Sleeping mats,Oceania,"Penama, Vanuatu",Vanuatu,159
+Tarpaulins,Oceania,"Penama, Vanuatu",Vanuatu,227
+Water Containers,Oceania,"Penama, Vanuatu",Vanuatu,215
+mosquito nets,Oceania,"Penama, Vanuatu",Vanuatu,190
+Blankets,Oceania,"Sanma, Vanuatu",Vanuatu,954
+Buckets,Oceania,"Sanma, Vanuatu",Vanuatu,209
+Dignity Kits,Oceania,"Sanma, Vanuatu",Vanuatu,295
+Hygiene Kits,Oceania,"Sanma, Vanuatu",Vanuatu,1175
+Kitchen sets,Oceania,"Sanma, Vanuatu",Vanuatu,601
+Shelter tool kits,Oceania,"Sanma, Vanuatu",Vanuatu,760
+Sleeping mats,Oceania,"Sanma, Vanuatu",Vanuatu,2400
+Tarpaulins,Oceania,"Sanma, Vanuatu",Vanuatu,2370
+Water Containers,Oceania,"Sanma, Vanuatu",Vanuatu,4260
+mosquito nets,Oceania,"Sanma, Vanuatu",Vanuatu,50
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,2808
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,2659
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,263
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,3913
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,953
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,27119
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,201
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,840
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,5301
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,7123
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,6000
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,2063
+Blankets,Oceania,"Tafea, Vanuatu",Vanuatu,243
+Dignity Kits,Oceania,"Tafea, Vanuatu",Vanuatu,199
+Hygiene Kits,Oceania,"Tafea, Vanuatu",Vanuatu,600
+Kitchen sets,Oceania,"Tafea, Vanuatu",Vanuatu,100
+Shelter tool kits,Oceania,"Tafea, Vanuatu",Vanuatu,136
+Sleeping mats,Oceania,"Tafea, Vanuatu",Vanuatu,125
+Tarpaulins,Oceania,"Tafea, Vanuatu",Vanuatu,1200
+Water Containers,Oceania,"Tafea, Vanuatu",Vanuatu,1180
+mosquito nets,Oceania,"Tafea, Vanuatu",Vanuatu,198
+Blankets,Oceania,"Torba, Vanuatu",Vanuatu,319
+Hygiene Kits,Oceania,"Torba, Vanuatu",Vanuatu,192
+Kitchen sets,Oceania,"Torba, Vanuatu",Vanuatu,168
+Shelter tool kits,Oceania,"Torba, Vanuatu",Vanuatu,254
+Sleeping mats,Oceania,"Torba, Vanuatu",Vanuatu,400
+Tarpaulins,Oceania,"Torba, Vanuatu",Vanuatu,466
+Water Containers,Oceania,"Torba, Vanuatu",Vanuatu,286
+mosquito nets,Oceania,"Torba, Vanuatu",Vanuatu,465
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_50pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_50pct.csv
new file mode 100644
index 0000000..37a94cb
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_50pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,6417
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,1540
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,770
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,3850
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,770
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,1540
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,3850
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,3850
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,770
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,3850
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,1540
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,770
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,1540
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_55pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_55pct.csv
new file mode 100644
index 0000000..4535c1c
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_55pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,14530
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,3487
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,1744
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,8718
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,1744
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,3487
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,8718
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,8718
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,1744
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,8718
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,3487
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,1744
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,3487
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_60pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_60pct.csv
new file mode 100644
index 0000000..6251db8
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_60pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,22103
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,5305
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,2652
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,13262
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,2652
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,5305
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,13262
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,13262
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,2652
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,13262
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,5305
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,2652
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,5305
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_65pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_65pct.csv
new file mode 100644
index 0000000..730c023
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_65pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,29900
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,7176
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,3588
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,17940
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,3588
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,7176
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,17940
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,17940
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,3588
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,17940
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,7176
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,3588
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,7176
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_70pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_70pct.csv
new file mode 100644
index 0000000..fa8600a
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_70pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,47337
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,11361
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,5680
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,28402
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,5680
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,11361
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,28402
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,28402
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,5680
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,28402
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,11361
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,5680
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,11361
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_75pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_75pct.csv
new file mode 100644
index 0000000..d243049
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_75pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,73333
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,17600
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,8800
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,44000
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,8800
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,17600
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,44000
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,44000
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,8800
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,44000
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,17600
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,8800
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,17600
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_80pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_80pct.csv
new file mode 100644
index 0000000..809025b
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_80pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,88012
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,21123
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,10561
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,52807
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,10561
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,21123
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,52807
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,52807
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,10561
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,52807
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,21123
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,10561
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,21123
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_85pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_85pct.csv
new file mode 100644
index 0000000..6f3d507
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_85pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,114112
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,27387
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,13693
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,68467
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,13693
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,27387
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,68467
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,68467
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,13693
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,68467
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,27387
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,13693
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,27387
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_90pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_90pct.csv
new file mode 100644
index 0000000..a46ece3
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_90pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,207017
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,49684
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,24842
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,124210
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,24842
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,49684
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,124210
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,124210
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,24842
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,124210
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,49684
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,24842
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,49684
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_95pct.csv b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_95pct.csv
new file mode 100644
index 0000000..20400a9
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/actual_95pct.csv
@@ -0,0 +1,14 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,308507
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,74042
+Hygiene Kits,Oceania,"Shefa, Vanuatu",Vanuatu,37021
+Dignity Kits,Oceania,"Shefa, Vanuatu",Vanuatu,185104
+Kitchen sets,Oceania,"Shefa, Vanuatu",Vanuatu,37021
+mosquito nets,Oceania,"Shefa, Vanuatu",Vanuatu,74042
+Personal Protection Equipment kit (PPE),Oceania,"Shefa, Vanuatu",Vanuatu,185104
+Water Purification Tablets,Oceania,"Shefa, Vanuatu",Vanuatu,185104
+Shelter tool kits,Oceania,"Shefa, Vanuatu",Vanuatu,37021
+Sleeping mats,Oceania,"Shefa, Vanuatu",Vanuatu,185104
+Tarpaulins,Oceania,"Shefa, Vanuatu",Vanuatu,74042
+Family Tents,Oceania,"Shefa, Vanuatu",Vanuatu,37021
+Water Containers,Oceania,"Shefa, Vanuatu",Vanuatu,74042
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/inventory/rm_data.py b/optimization202/ESUPS_case_study/data/vanuatu/inventory/rm_data.py
new file mode 100644
index 0000000..db5fb01
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/inventory/rm_data.py
@@ -0,0 +1,13 @@
+import pandas as pd
+
+df=pd.read_csv('actual.csv')
+print(df)
+
+# Drop rows where ItemName is not 'Blankets' or 'Bucket'
+df_filtered = df[df['ItemName'].isin(['Blankets', 'Bucket'])]
+
+# Display filtered DataFrame
+print("\nFiltered DataFrame:")
+print(df_filtered)
+
+df_filtered.to_csv('actual.csv', index=False)
\ No newline at end of file
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/items.csv b/optimization202/ESUPS_case_study/data/vanuatu/items.csv
new file mode 100644
index 0000000..6c91933
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/items.csv
@@ -0,0 +1,14 @@
+ItemName,WeightMetricTon,CubicMeters
+Blankets,0.0015,0.01
+Buckets,0.00091,0.00617
+Dignity Kits,0.0056,0.0262
+Family Tents,0.0528,0.19
+Hygiene Kits,0.0056,0.0262
+Kitchen sets,0.00499,0.01763
+mosquito nets,0.0004435,0.00135
+Personal Protection Equipment kit (PPE),0.0035,0.0000288
+Shelter tool kits,0.010421,0.02819
+Sleeping mats,0.00144,0.00745
+Water Containers,0.0001738,0.0021
+Water Purification Tablets,0.00000577,0.00003
+Tarpaulins,0.0041626,0.00943
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/personsPerItem.csv b/optimization202/ESUPS_case_study/data/vanuatu/personsPerItem.csv
new file mode 100644
index 0000000..7599498
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/personsPerItem.csv
@@ -0,0 +1,29 @@
+Item,Disaster Type,Month,gglCountry,PersonsPerItem
+Blankets,DEFAULT,DEFAULT,DEFAULT,0.6
+Buckets,DEFAULT,DEFAULT,DEFAULT,2.5
+Dignity Kits,DEFAULT,DEFAULT,DEFAULT,1
+Family Tents,DEFAULT,DEFAULT,DEFAULT,5
+Hygiene Kits,DEFAULT,DEFAULT,DEFAULT,5
+Kitchen sets,DEFAULT,DEFAULT,DEFAULT,5
+mosquito nets,DEFAULT,DEFAULT,DEFAULT,2.5
+Personal Protection Equipment kit (PPE),DEFAULT,DEFAULT,DEFAULT,1
+Shelter tool kits,DEFAULT,DEFAULT,DEFAULT,5
+Solar Lights,DEFAULT,DEFAULT,DEFAULT,5
+Sleeping mats,DEFAULT,DEFAULT,DEFAULT,5
+Tarpaulins,DEFAULT,DEFAULT,DEFAULT,2.5
+Water Containers,DEFAULT,DEFAULT,DEFAULT,2.5
+Water Purification Tablets,DEFAULT,DEFAULT,DEFAULT,1
+Blankets,DEFAULT,-1,DEFAULT,0.6
+Buckets,DEFAULT,-1,DEFAULT,2.5
+Dignity Kits,DEFAULT,-1,DEFAULT,1
+Family Tents,DEFAULT,-1,DEFAULT,5
+Hygiene Kits,DEFAULT,-1,DEFAULT,5
+Kitchen sets,DEFAULT,-1,DEFAULT,5
+mosquito nets,DEFAULT,-1,DEFAULT,2.5
+Personal Protection Equipment kit (PPE),DEFAULT,-1,DEFAULT,1
+Shelter tool kits,DEFAULT,-1,DEFAULT,5
+Solar Lights,DEFAULT,-1,DEFAULT,5
+Sleeping mats,DEFAULT,-1,DEFAULT,5
+Tarpaulins,DEFAULT,-1,DEFAULT,2.5
+Water Containers,DEFAULT,-1,DEFAULT,2.5
+Water Purification Tablets,DEFAULT,-1,DEFAULT,1
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/province_lookup.csv b/optimization202/ESUPS_case_study/data/vanuatu/province_lookup.csv
new file mode 100644
index 0000000..24e7618
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/province_lookup.csv
@@ -0,0 +1,7 @@
+province,warehouse_id
+Torba,Torba
+Sanma,Sanma
+Penama,Penama
+Malampa,Malampa
+Shefa,Shefa
+Tafea,Tafea
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/transportModes.csv b/optimization202/ESUPS_case_study/data/vanuatu/transportModes.csv
new file mode 100644
index 0000000..35c098e
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/transportModes.csv
@@ -0,0 +1,3 @@
+Mode,DistanceMethod,BigMCostElim,MaxDrivingTimeCutAboveHrs
+Truck,google,1e9,100
+Air,crowScale,1e9,100
diff --git a/optimization202/ESUPS_case_study/data/vanuatu/transportParameters.csv b/optimization202/ESUPS_case_study/data/vanuatu/transportParameters.csv
new file mode 100644
index 0000000..9bc70ca
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu/transportParameters.csv
@@ -0,0 +1,12 @@
+Attribute,Mode,gglAddress,Number
+FixedAddlTime_Hrs,Truck,DEFAULT,24
+FixedAddlTime_Hrs,Air,DEFAULT,100000000
+StretchTimeFactor,Truck,DEFAULT,1.1
+StretchTimeFactor,Air,DEFAULT,100000000
+SpeedKmPerHr,Air,DEFAULT,0.000001
+FixedAddlCost_USD,Truck,DEFAULT,600
+FixedAddlCost_USD,Air,DEFAULT,1.00E+08
+VarCost_USD_ton_km,Truck,DEFAULT,1.2
+VarCost_USD_ton_km,Air,DEFAULT,1.00E+08
+StretchDistanceFactor,Truck,DEFAULT,1
+StretchDistanceFactor,Air,DEFAULT,1
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/abilityToRespond.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/abilityToRespond.csv
new file mode 100644
index 0000000..958db29
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/abilityToRespond.csv
@@ -0,0 +1,216 @@
+gglCountry,item,capacityToRespond
+Afghanistan,DEFAULT,10
+Albania,DEFAULT,10
+Algeria,DEFAULT,10
+American Samoa,DEFAULT,10
+Angola,DEFAULT,10
+Anguilla,DEFAULT,10
+Antigua and Barbuda,DEFAULT,10
+Argentina,DEFAULT,10
+Armenia,DEFAULT,10
+Australia,DEFAULT,2.00E+15
+Austria,DEFAULT,2.00E+15
+Azerbaijan,DEFAULT,10
+Bahrain,DEFAULT,10
+Bangladesh,DEFAULT,10
+Barbados,DEFAULT,10
+Belarus,DEFAULT,10
+Belgium,DEFAULT,2.00E+15
+Belize,DEFAULT,10
+Benin,DEFAULT,10
+Bermuda,DEFAULT,10
+Bhutan,DEFAULT,10
+Bolivia,DEFAULT,10
+Bosnia and Herzegovina,DEFAULT,10
+Botswana,DEFAULT,10
+Brazil,DEFAULT,10
+British Virgin Islands,DEFAULT,10
+Brunei,DEFAULT,10
+Bulgaria,DEFAULT,2.00E+15
+Burkina Faso,DEFAULT,10
+Burundi,DEFAULT,10
+Cambodia,DEFAULT,10
+Cameroon,DEFAULT,10
+Canada,DEFAULT,2.00E+15
+Cape Verde,DEFAULT,10
+Cayman Islands,DEFAULT,10
+Central African Republic,DEFAULT,10
+Chad,DEFAULT,10
+Chile,DEFAULT,10
+China,DEFAULT,10
+Colombia,DEFAULT,10
+Comoros,DEFAULT,10
+Congo,DEFAULT,10
+Cook Islands,DEFAULT,10
+Costa Rica,DEFAULT,10
+Cote d'Ivoire,DEFAULT,10
+Croatia,DEFAULT,2.00E+15
+Cuba,DEFAULT,10
+Curacao,DEFAULT,10
+Czech Republic,DEFAULT,2.00E+15
+Democratic Republic of the Congo,DEFAULT,10
+Denmark,DEFAULT,2.00E+15
+Djibouti,DEFAULT,10
+Dominica,DEFAULT,10
+Dominican Republic,DEFAULT,10
+Ecuador,DEFAULT,10
+Egypt,DEFAULT,10
+El Salvador,DEFAULT,10
+Equatorial Guinea,DEFAULT,10
+Eritrea,DEFAULT,10
+Estonia,DEFAULT,2.00E+15
+Ethiopia,DEFAULT,10
+Fiji,DEFAULT,10
+Finland,DEFAULT,2.00E+15
+France,DEFAULT,2.00E+15
+French Guiana,DEFAULT,10
+French Polynesia,DEFAULT,10
+Gabon,DEFAULT,10
+Georgia,DEFAULT,10
+Germany,DEFAULT,2.00E+15
+Ghana,DEFAULT,10
+Greece,DEFAULT,2.00E+15
+Grenada,DEFAULT,10
+Guadeloupe,DEFAULT,10
+Guam,DEFAULT,10
+Guatemala,DEFAULT,10
+Guinea,DEFAULT,10
+Guinea-Bissau,DEFAULT,10
+Guyana,DEFAULT,10
+Haiti,DEFAULT,10
+Honduras,DEFAULT,10
+Hong Kong,DEFAULT,10
+Hungary,DEFAULT,2.00E+15
+Iceland,DEFAULT,2.00E+15
+India,DEFAULT,10
+Indonesia,DEFAULT,10
+Iran,DEFAULT,10
+Iraq,DEFAULT,10
+Ireland,DEFAULT,2.00E+15
+Israel,DEFAULT,10
+Italy,DEFAULT,2.00E+15
+Jamaica,DEFAULT,10
+Japan,DEFAULT,10
+Jordan,DEFAULT,10
+Kazakhstan,DEFAULT,10
+Kenya,DEFAULT,10
+Kiribati,DEFAULT,10
+Kuwait,DEFAULT,10
+Kyrgyzstan,DEFAULT,10
+Laos,DEFAULT,10
+Latvia,DEFAULT,2.00E+15
+Lebanon,DEFAULT,10
+Lesotho,DEFAULT,10
+Liberia,DEFAULT,10
+Libya,DEFAULT,10
+Lithuania,DEFAULT,2.00E+15
+Luxembourg,DEFAULT,2.00E+15
+Macau,DEFAULT,10
+Macedonia (FYROM),DEFAULT,10
+Madagascar,DEFAULT,10
+Malawi,DEFAULT,10
+Malaysia,DEFAULT,10
+Maldives,DEFAULT,10
+Mali,DEFAULT,10
+Malta,DEFAULT,2.00E+15
+Marshall Islands,DEFAULT,10
+Martinique,DEFAULT,10
+Mauritania,DEFAULT,10
+Mauritius,DEFAULT,10
+Mayotte,DEFAULT,10
+Mexico,DEFAULT,10
+Micronesia,DEFAULT,10
+Moldova,DEFAULT,10
+Mongolia,DEFAULT,10
+Montenegro,DEFAULT,10
+Montserrat,DEFAULT,10
+Morocco,DEFAULT,10
+Mozambique,DEFAULT,10
+Namibia,DEFAULT,10
+Nepal,DEFAULT,10
+New Caledonia,DEFAULT,10
+New Zealand,DEFAULT,2.00E+15
+Nicaragua,DEFAULT,10
+Nicosia,DEFAULT,2.00E+15
+Niger,DEFAULT,10
+Nigeria,DEFAULT,10
+Niue,DEFAULT,10
+North Korea,DEFAULT,10
+Northern Mariana Islands,DEFAULT,10
+Norway,DEFAULT,2.00E+15
+Oman,DEFAULT,10
+Pakistan,DEFAULT,10
+Palau,DEFAULT,10
+Panama,DEFAULT,10
+Papua New Guinea,DEFAULT,10
+Paraguay,DEFAULT,10
+Peru,DEFAULT,10
+Philippines,DEFAULT,10
+Poland,DEFAULT,2.00E+15
+Portugal,DEFAULT,2.00E+15
+Puerto Rico,DEFAULT,10
+Qatar,DEFAULT,10
+Republic of the Union of Myanmar,DEFAULT,10
+Reunion,DEFAULT,10
+Romania,DEFAULT,2.00E+15
+Russia,DEFAULT,10
+Rwanda,DEFAULT,10
+Saint Helena,DEFAULT,10
+Saint Kitts and Nevis,DEFAULT,10
+Saint Lucia,DEFAULT,10
+Saint Vincent and the Grenadines,DEFAULT,10
+Samoa,DEFAULT,10
+Sao Tome and Principe,DEFAULT,10
+Saudi Arabia,DEFAULT,10
+Senegal,DEFAULT,10
+Serbia,DEFAULT,10
+Seychelles,DEFAULT,10
+Sierra Leone,DEFAULT,10
+Singapore,DEFAULT,10
+Slovakia,DEFAULT,2.00E+15
+Slovenia,DEFAULT,2.00E+15
+Solomon Islands,DEFAULT,10
+Somalia,DEFAULT,10
+South Africa,DEFAULT,10
+South Korea,DEFAULT,10
+South Sudan,DEFAULT,10
+Spain,DEFAULT,2.00E+15
+Sri Lanka,DEFAULT,10
+Sudan,DEFAULT,10
+Suriname,DEFAULT,10
+Swaziland,DEFAULT,10
+Sweden,DEFAULT,2.00E+15
+Switzerland,DEFAULT,2.00E+15
+Syria,DEFAULT,10
+Taiwan,DEFAULT,10
+Tajikistan,DEFAULT,10
+Tanzania,DEFAULT,10
+Thailand,DEFAULT,10
+The Bahamas,DEFAULT,10
+The Gambia,DEFAULT,10
+The Netherlands,DEFAULT,2.00E+15
+Timor-Leste,DEFAULT,10
+Togo,DEFAULT,10
+Tokelau,DEFAULT,10
+Tonga,DEFAULT,10
+Trinidad and Tobago,DEFAULT,10
+Tunisia,DEFAULT,10
+Turkey,DEFAULT,10
+Turkmenistan,DEFAULT,10
+Turks and Caicos Islands,DEFAULT,10
+Tuvalu,DEFAULT,10
+U.S. Virgin Islands,DEFAULT,10
+Uganda,DEFAULT,10
+Ukraine,DEFAULT,10
+United Arab Emirates,DEFAULT,10
+United Kingdom,DEFAULT,2.00E+15
+United States,DEFAULT,2.00E+15
+Uruguay,DEFAULT,10
+Uzbekistan,DEFAULT,10
+Vanuatu,DEFAULT,10
+Venezuela,DEFAULT,10
+Vietnam,DEFAULT,10
+Wallis and Futuna,DEFAULT,10
+Yemen,DEFAULT,10
+Zambia,DEFAULT,10
+Zimbabwe,DEFAULT,10
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/countryContinents.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/countryContinents.csv
new file mode 100644
index 0000000..43921a4
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/countryContinents.csv
@@ -0,0 +1,216 @@
+gglCountry,Continent
+Central African Republic,Africa
+Sao Tome and Principe,Africa
+Cote d'Ivoire,Africa
+Libya,Africa
+Democratic Republic of the Congo,Africa
+The Gambia,Africa
+Guinea-Bissau,Africa
+Cape Verde,Africa
+Reunion,Africa
+Saint Helena,Africa
+Mayotte,Africa
+Tanzania,Africa
+South Sudan,Africa
+Congo,Africa
+Algeria,Africa
+Angola,Africa
+Benin,Africa
+Botswana,Africa
+Burkina Faso,Africa
+Burundi,Africa
+Cameroon,Africa
+Chad,Africa
+Comoros,Africa
+Djibouti,Africa
+Egypt,Africa
+Equatorial Guinea,Africa
+Eritrea,Africa
+Ethiopia,Africa
+Gabon,Africa
+Ghana,Africa
+Guinea,Africa
+Kenya,Africa
+Lesotho,Africa
+Liberia,Africa
+Madagascar,Africa
+Malawi,Africa
+Mali,Africa
+Mauritania,Africa
+Mauritius,Africa
+Morocco,Africa
+Mozambique,Africa
+Namibia,Africa
+Niger,Africa
+Nigeria,Africa
+Rwanda,Africa
+Senegal,Africa
+Seychelles,Africa
+Sierra Leone,Africa
+Somalia,Africa
+South Africa,Africa
+Sudan,Africa
+Swaziland,Africa
+Togo,Africa
+Tunisia,Africa
+Uganda,Africa
+Zambia,Africa
+Zimbabwe,Africa
+Taiwan,Asia
+Macau,Asia
+North Korea,Asia
+Vietnam,Asia
+Timor-Leste,Asia
+Syria,Asia
+Brunei,Asia
+Iran,Asia
+South Korea,Asia
+Israel,Asia
+Yemen,Asia
+Hong Kong,Asia
+Laos,Asia
+China,Asia
+Russia,Asia
+Afghanistan,Asia
+Bahrain,Asia
+Bangladesh,Asia
+Bhutan,Asia
+Cambodia,Asia
+India,Asia
+Indonesia,Asia
+Iraq,Asia
+Japan,Asia
+Jordan,Asia
+Kazakhstan,Asia
+Kuwait,Asia
+Kyrgyzstan,Asia
+Lebanon,Asia
+Malaysia,Asia
+Maldives,Asia
+Mongolia,Asia
+Republic of the Union of Myanmar,Asia
+Nepal,Asia
+Oman,Asia
+Pakistan,Asia
+Philippines,Asia
+Qatar,Asia
+Saudi Arabia,Asia
+Singapore,Asia
+Sri Lanka,Asia
+Tajikistan,Asia
+Thailand,Asia
+Turkey,Asia
+Turkmenistan,Asia
+United Arab Emirates,Asia
+Uzbekistan,Asia
+Germany,Europe
+Portugal,Europe
+Czech Republic,Europe
+Moldova,Europe
+Macedonia (FYROM),Europe
+Bosnia and Herzegovina,Europe
+Spain,Europe
+United Kingdom,Europe
+Serbia,Europe
+Albania,Europe
+Armenia,Europe
+Austria,Europe
+Azerbaijan,Europe
+Belarus,Europe
+Belgium,Europe
+Bulgaria,Europe
+Croatia,Europe
+Nicosia,Europe
+Denmark,Europe
+Estonia,Europe
+Finland,Europe
+France,Europe
+Georgia,Europe
+Greece,Europe
+Hungary,Europe
+Iceland,Europe
+Ireland,Europe
+Italy,Europe
+Latvia,Europe
+Lithuania,Europe
+Luxembourg,Europe
+Malta,Europe
+Montenegro,Europe
+The Netherlands,Europe
+Norway,Europe
+Poland,Europe
+Romania,Europe
+Slovakia,Europe
+Slovenia,Europe
+Sweden,Europe
+Switzerland,Europe
+Ukraine,Europe
+Saint Vincent and the Grenadines,NorthAmerica
+British Virgin Islands,NorthAmerica
+Dominican Republic,NorthAmerica
+U.S. Virgin Islands,NorthAmerica
+Curacao,NorthAmerica
+Turks and Caicos Islands,NorthAmerica
+Saint Kitts and Nevis,NorthAmerica
+Martinique,NorthAmerica
+Saint Lucia,NorthAmerica
+Guadeloupe,NorthAmerica
+Anguilla,NorthAmerica
+Antigua and Barbuda,NorthAmerica
+The Bahamas,NorthAmerica
+Barbados,NorthAmerica
+Belize,NorthAmerica
+Bermuda,NorthAmerica
+Canada,NorthAmerica
+Cayman Islands,NorthAmerica
+Costa Rica,NorthAmerica
+Cuba,NorthAmerica
+Dominica,NorthAmerica
+El Salvador,NorthAmerica
+Grenada,NorthAmerica
+Guatemala,NorthAmerica
+Haiti,NorthAmerica
+Honduras,NorthAmerica
+Jamaica,NorthAmerica
+Mexico,NorthAmerica
+Montserrat,NorthAmerica
+Nicaragua,NorthAmerica
+Panama,NorthAmerica
+Puerto Rico,NorthAmerica
+Trinidad and Tobago,NorthAmerica
+United States,NorthAmerica
+Wallis and Futuna,Oceania
+Marshall Islands,Oceania
+Cook Islands,Oceania
+Northern Mariana Islands,Oceania
+Tokelau,Oceania
+Solomon Islands,Oceania
+Micronesia,Oceania
+American Samoa,Oceania
+Australia,Oceania
+Fiji,Oceania
+French Polynesia,Oceania
+Guam,Oceania
+Kiribati,Oceania
+New Caledonia,Oceania
+New Zealand,Oceania
+Niue,Oceania
+Palau,Oceania
+Papua New Guinea,Oceania
+Samoa,Oceania
+Tonga,Oceania
+Tuvalu,Oceania
+Vanuatu,Oceania
+Argentina,SouthAmerica
+Bolivia,SouthAmerica
+Brazil,SouthAmerica
+Chile,SouthAmerica
+Colombia,SouthAmerica
+Ecuador,SouthAmerica
+French Guiana,SouthAmerica
+Guyana,SouthAmerica
+Paraguay,SouthAmerica
+Peru,SouthAmerica
+Suriname,SouthAmerica
+Uruguay,SouthAmerica
+Venezuela,SouthAmerica
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/depotCoordinates.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/depotCoordinates.csv
new file mode 100644
index 0000000..bf93432
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/depotCoordinates.csv
@@ -0,0 +1,7 @@
+depotCity,gglLat,gglLong,gglCountryAscii,gglAddressAscii
+Torba,-13.873643999999956,167.5495830000001,Vanuatu,"Torba, Vanuatu"
+Sanma,-15.513757933999957,167.1799922040001,Vanuatu,"Sanma, Vanuatu"
+Penama,-15.548909039999955,168.1484143,Vanuatu,"Penama, Vanuatu"
+Malampa,-16.10622799099997,167.41678100600006,Vanuatu,"Malampa, Vanuatu"
+Shefa,-17.73763299799998,168.3157348850001,Vanuatu,"Shefa, Vanuatu"
+Tafea,-19.54447261799993,169.2801722150001,Vanuatu,"Tafea, Vanuatu"
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/depotMapping.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/depotMapping.csv
new file mode 100644
index 0000000..63bcef6
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/depotMapping.csv
@@ -0,0 +1,7 @@
+gglAddressAsciiMapFrom,gglAddressAsciiMapTo
+"Torba, Vanuatu","Torba, Vanuatu"
+"Sanma, Vanuatu","Sanma, Vanuatu"
+"Penama, Vanuatu","Penama, Vanuatu"
+"Malampa, Vanuatu","Malampa, Vanuatu"
+"Shefa, Vanuatu","Shefa, Vanuatu"
+"Tafea, Vanuatu","Tafea, Vanuatu"
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/depotSelection.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/depotSelection.csv
new file mode 100644
index 0000000..7cb5bda
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/depotSelection.csv
@@ -0,0 +1,7 @@
+gglAddressAscii,gglCountry,include
+"Torba, Vanuatu",Vanuatu,1
+"Sanma, Vanuatu",Vanuatu,1
+"Penama, Vanuatu",Vanuatu,1
+"Malampa, Vanuatu",Vanuatu,1
+"Shefa, Vanuatu",Vanuatu,1
+"Tafea, Vanuatu",Vanuatu,1
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/disasterCoordinates.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/disasterCoordinates.csv
new file mode 100644
index 0000000..cad4a3f
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/disasterCoordinates.csv
@@ -0,0 +1,7 @@
+emdatCity,gglLat,gglLong,gglCountryAscii,gglAddressAscii
+Torba,-13.873643999999956,167.5495830000001,Vanuatu,"Torba, Vanuatu"
+Sanma,-15.513757933999957,167.1799922040001,Vanuatu,"Sanma, Vanuatu"
+Penama,-15.548909039999955,168.1484143,Vanuatu,"Penama, Vanuatu"
+Malampa,-16.10622799099997,167.41678100600006,Vanuatu,"Malampa, Vanuatu"
+Shefa,-17.73763299799998,168.3157348850001,Vanuatu,"Shefa, Vanuatu"
+Tafea,-19.54447261799993,169.2801722150001,Vanuatu,"Tafea, Vanuatu"
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/disasterTypeSelection.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/disasterTypeSelection.csv
new file mode 100644
index 0000000..b1e235a
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/disasterTypeSelection.csv
@@ -0,0 +1,22 @@
+disasterType,include
+Complex Disasters,
+Drought,
+Earthquake (seismic activity),1
+Earthquake,1
+Epidemic,1
+Extreme temperature,
+Flood,1
+Industrial Accident,
+Insect Infestation,
+Insect infestation,
+Landslide,1
+Mass movement dry,1
+Mass Movement Dry,1
+Mass Movement Wet,1
+Mass movement wet,1
+Miscellaneous accident,
+Storm,1
+Transport accident,
+Transport Accident,
+Volcano,1
+Wildfire,1
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/disasters.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/disasters.csv
new file mode 100644
index 0000000..8061f64
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/disasters.csv
@@ -0,0 +1,64 @@
+Day,Month,Year,origDistrict,Type,DisasterID,TotAffected,gglCountry,gglAddress
+16.0,1.0,1985,Penama,Storm,1985-0020-VUT,28870.0,Vanuatu,"Penama, Vanuatu"
+16.0,1.0,1985,Sanma,Storm,1985-0020-VUT,42148.0,Vanuatu,"Sanma, Vanuatu"
+7.0,2.0,1987,Shefa,Storm,1987-0057-VUT,33969.0,Vanuatu,"Shefa, Vanuatu"
+7.0,2.0,1987,Tafea,Storm,1987-0057-VUT,14031.0,Vanuatu,"Tafea, Vanuatu"
+11.0,1.0,1988,Sanma,Storm,1988-0040-VUT,3903.0,Vanuatu,"Sanma, Vanuatu"
+11.0,1.0,1988,Torba,Storm,1988-0040-VUT,797.0,Vanuatu,"Torba, Vanuatu"
+,8.0,1988,Malampa,Storm,1988-0676-VUT,636.0,Vanuatu,"Malampa, Vanuatu"
+,8.0,1988,Shefa,Storm,1988-0676-VUT,1364.0,Vanuatu,"Shefa, Vanuatu"
+9.0,3.0,1992,Shefa,Storm,1992-0050-VUT,1150.0,Vanuatu,"Shefa, Vanuatu"
+30.0,3.0,1993,Shefa,Storm,1993-0024-VUT,12005.0,Vanuatu,"Shefa, Vanuatu"
+21.0,3.0,1998,Sanma,Storm,1998-0090-VUT,1254.0,Vanuatu,"Sanma, Vanuatu"
+21.0,3.0,1998,Tafea,Storm,1998-0090-VUT,890.0,Vanuatu,"Tafea, Vanuatu"
+21.0,3.0,1998,Torba,Storm,1998-0090-VUT,256.0,Vanuatu,"Torba, Vanuatu"
+27.0,11.0,1999,Malampa,Earthquake,1999-0524-VUT,3526.0,Vanuatu,"Malampa, Vanuatu"
+27.0,11.0,1999,Penama,Earthquake,1999-0524-VUT,3016.0,Vanuatu,"Penama, Vanuatu"
+27.0,11.0,1999,Shefa,Earthquake,1999-0524-VUT,7558.0,Vanuatu,"Shefa, Vanuatu"
+7.0,4.0,2001,Malampa,Storm,2001-0143-VUT,130.0,Vanuatu,"Malampa, Vanuatu"
+7.0,4.0,2001,Penama,Storm,2001-0143-VUT,112.0,Vanuatu,"Penama, Vanuatu"
+7.0,4.0,2001,Sanma,Storm,2001-0143-VUT,163.0,Vanuatu,"Sanma, Vanuatu"
+7.0,4.0,2001,Shefa,Storm,2001-0143-VUT,280.0,Vanuatu,"Shefa, Vanuatu"
+7.0,4.0,2001,Tafea,Storm,2001-0143-VUT,115.0,Vanuatu,"Tafea, Vanuatu"
+8.0,6.0,2001,Malampa,Volcanic activity,2001-0254-VUT,1627.0,Vanuatu,"Malampa, Vanuatu"
+3.0,1.0,2002,Shefa,Earthquake,2002-0002-VUT,500.0,Vanuatu,"Shefa, Vanuatu"
+27.0,11.0,2002,Torba,Earthquake,2002-0770-VUT,503.0,Vanuatu,"Torba, Vanuatu"
+21.0,12.0,2002,Tafea,Flood,2002-0829-VUT,3001.0,Vanuatu,"Tafea, Vanuatu"
+25.0,2.0,2004,Malampa,Storm,2004-0080-VUT,11057.0,Vanuatu,"Malampa, Vanuatu"
+25.0,2.0,2004,Penama,Storm,2004-0080-VUT,9458.0,Vanuatu,"Penama, Vanuatu"
+25.0,2.0,2004,Shefa,Storm,2004-0080-VUT,23703.0,Vanuatu,"Shefa, Vanuatu"
+25.0,2.0,2004,Tafea,Storm,2004-0080-VUT,9791.0,Vanuatu,"Tafea, Vanuatu"
+27.0,11.0,2005,Penama,Volcanic activity,2005-0664-VUT,5000.0,Vanuatu,"Penama, Vanuatu"
+9.0,5.0,2006,Malampa,Volcanic activity,2006-0281-VUT,1572.0,Vanuatu,"Malampa, Vanuatu"
+,12.0,2008,Malampa,Volcanic activity,2008-0659-VUT,7275.0,Vanuatu,"Malampa, Vanuatu"
+15.0,4.0,2009,Malampa,Flood,2009-0171-VUT,950.0,Vanuatu,"Malampa, Vanuatu"
+26.0,11.0,2009,Torba,Volcanic activity,2009-0553-VUT,400.0,Vanuatu,"Torba, Vanuatu"
+12.0,1.0,2011,Tafea,Storm,2011-0071-VUT,32000.0,Vanuatu,"Tafea, Vanuatu"
+9.0,3.0,2014,Malampa,Storm,2014-0096-VUT,3636.0,Vanuatu,"Malampa, Vanuatu"
+9.0,3.0,2014,Penama,Storm,2014-0096-VUT,3110.0,Vanuatu,"Penama, Vanuatu"
+9.0,3.0,2014,Sanma,Storm,2014-0096-VUT,4540.0,Vanuatu,"Sanma, Vanuatu"
+9.0,3.0,2014,Shefa,Storm,2014-0096-VUT,7794.0,Vanuatu,"Shefa, Vanuatu"
+9.0,3.0,2014,Torba,Storm,2014-0096-VUT,927.0,Vanuatu,"Torba, Vanuatu"
+12.0,3.0,2015,Malampa,Storm,2015-0093-VUT,29428.0,Vanuatu,"Malampa, Vanuatu"
+12.0,3.0,2015,Penama,Storm,2015-0093-VUT,25173.0,Vanuatu,"Penama, Vanuatu"
+12.0,3.0,2015,Sanma,Storm,2015-0093-VUT,36751.0,Vanuatu,"Sanma, Vanuatu"
+12.0,3.0,2015,Shefa,Storm,2015-0093-VUT,63087.0,Vanuatu,"Shefa, Vanuatu"
+12.0,3.0,2015,Tafea,Storm,2015-0093-VUT,26059.0,Vanuatu,"Tafea, Vanuatu"
+12.0,3.0,2015,Torba,Storm,2015-0093-VUT,7500.0,Vanuatu,"Torba, Vanuatu"
+30.0,11.0,2016,Malampa,Epidemic,2016-0528-VUT,63.0,Vanuatu,"Malampa, Vanuatu"
+30.0,11.0,2016,Sanma,Epidemic,2016-0528-VUT,79.0,Vanuatu,"Sanma, Vanuatu"
+30.0,11.0,2016,Shefa,Epidemic,2016-0528-VUT,136.0,Vanuatu,"Shefa, Vanuatu"
+30.0,11.0,2016,Tafea,Epidemic,2016-0528-VUT,56.0,Vanuatu,"Tafea, Vanuatu"
+30.0,11.0,2016,Torba,Epidemic,2016-0528-VUT,16.0,Vanuatu,"Torba, Vanuatu"
+7.0,5.0,2017,Malampa,Storm,2017-0231-VUT,1024.0,Vanuatu,"Malampa, Vanuatu"
+7.0,5.0,2017,Sanma,Storm,2017-0231-VUT,1279.0,Vanuatu,"Sanma, Vanuatu"
+7.0,5.0,2017,Torba,Storm,2017-0231-VUT,261.0,Vanuatu,"Torba, Vanuatu"
+23.0,9.0,2017,Penama,Volcanic activity,2017-0394-VUT,11000.0,Vanuatu,"Penama, Vanuatu"
+,3.0,2018,Penama,Volcanic activity,2018-0147-VUT,6300.0,Vanuatu,"Penama, Vanuatu"
+15.0,12.0,2018,Malampa,Volcanic activity,2018-0456-VUT,7286.0,Vanuatu,"Malampa, Vanuatu"
+6.0,4.0,2020,Malampa,Storm,2020-0132-VUT,23646.0,Vanuatu,"Malampa, Vanuatu"
+6.0,4.0,2020,Penama,Storm,2020-0132-VUT,20227.0,Vanuatu,"Penama, Vanuatu"
+6.0,4.0,2020,Sanma,Storm,2020-0132-VUT,29530.0,Vanuatu,"Sanma, Vanuatu"
+6.0,4.0,2020,Shefa,Storm,2020-0132-VUT,50691.0,Vanuatu,"Shefa, Vanuatu"
+6.0,4.0,2020,Torba,Storm,2020-0132-VUT,6026.0,Vanuatu,"Torba, Vanuatu"
+21.0,10.0,2021,Tafea,Volcanic activity,2021-0847-VUT,3586.0,Vanuatu,"Tafea, Vanuatu"
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/distanceMatrix.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/distanceMatrix.csv
new file mode 100644
index 0000000..df72334
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/distanceMatrix.csv
@@ -0,0 +1,37 @@
+emdatCity,depotCity,distance_km,drivingTime_hrs,depotCountry,emdatCountry,gglCountryAscii_depot,gglCountryAscii_disaster,depotGglAddressAscii,disasterGglAddressAscii,depotGglLat,depotGglLong,disasterGglLat,disasterGglLong
+Torba,Torba,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Torba, Vanuatu",-13.873643999999956,167.5495830000001,-13.873643999999956,167.5495830000001
+Torba,Sanma,184.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Torba, Vanuatu",-15.513757933999955,167.1799922040001,-13.873643999999956,167.5495830000001
+Torba,Penama,195.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Torba, Vanuatu",-15.548909039999955,168.1484143,-13.873643999999956,167.5495830000001
+Torba,Malampa,241.0,16.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Torba, Vanuatu",-16.10622799099997,167.41678100600006,-13.873643999999956,167.5495830000001
+Torba,Shefa,430.0,26.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Torba, Vanuatu",-17.73763299799998,168.3157348850001,-13.873643999999956,167.5495830000001
+Torba,Tafea,657.0,36.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Torba, Vanuatu",-19.54447261799993,169.2801722150001,-13.873643999999956,167.5495830000001
+Sanma,Torba,184.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Sanma, Vanuatu",-13.873643999999956,167.5495830000001,-15.513757933999955,167.1799922040001
+Sanma,Sanma,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Sanma, Vanuatu",-15.513757933999955,167.1799922040001,-15.513757933999955,167.1799922040001
+Sanma,Penama,103.0,9.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Sanma, Vanuatu",-15.548909039999955,168.1484143,-15.513757933999955,167.1799922040001
+Sanma,Malampa,68.0,4.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Sanma, Vanuatu",-16.10622799099997,167.41678100600006,-15.513757933999955,167.1799922040001
+Sanma,Shefa,274.0,16.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Sanma, Vanuatu",-17.73763299799998,168.3157348850001,-15.513757933999955,167.1799922040001
+Sanma,Tafea,499.0,28.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Sanma, Vanuatu",-19.54447261799993,169.2801722150001,-15.513757933999955,167.1799922040001
+Penama,Torba,195.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Penama, Vanuatu",-13.873643999999956,167.5495830000001,-15.548909039999955,168.1484143
+Penama,Sanma,103.0,9.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Penama, Vanuatu",-15.513757933999955,167.1799922040001,-15.548909039999955,168.1484143
+Penama,Penama,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Penama, Vanuatu",-15.548909039999955,168.1484143,-15.548909039999955,168.1484143
+Penama,Malampa,97.0,6.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Penama, Vanuatu",-16.10622799099997,167.41678100600006,-15.548909039999955,168.1484143
+Penama,Shefa,244.0,14.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Penama, Vanuatu",-17.73763299799998,168.3157348850001,-15.548909039999955,168.1484143
+Penama,Tafea,461.0,26.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Penama, Vanuatu",-19.54447261799993,169.2801722150001,-15.548909039999955,168.1484143
+Malampa,Torba,241.0,16.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Malampa, Vanuatu",-13.873643999999956,167.5495830000001,-16.10622799099997,167.41678100600006
+Malampa,Sanma,68.0,4.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Malampa, Vanuatu",-15.513757933999955,167.1799922040001,-16.10622799099997,167.41678100600006
+Malampa,Penama,97.0,6.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Malampa, Vanuatu",-15.548909039999955,168.1484143,-16.10622799099997,167.41678100600006
+Malampa,Malampa,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Malampa, Vanuatu",-16.10622799099997,167.41678100600006,-16.10622799099997,167.41678100600006
+Malampa,Shefa,205.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Malampa, Vanuatu",-17.73763299799998,168.3157348850001,-16.10622799099997,167.41678100600006
+Malampa,Tafea,431.0,24.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Malampa, Vanuatu",-19.54447261799993,169.2801722150001,-16.10622799099997,167.41678100600006
+Shefa,Torba,430.0,26.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Shefa, Vanuatu",-13.873643999999956,167.5495830000001,-17.73763299799998,168.3157348850001
+Shefa,Sanma,274.0,16.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Shefa, Vanuatu",-15.513757933999955,167.1799922040001,-17.73763299799998,168.3157348850001
+Shefa,Penama,244.0,14.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Shefa, Vanuatu",-15.548909039999955,168.1484143,-17.73763299799998,168.3157348850001
+Shefa,Malampa,205.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Shefa, Vanuatu",-16.10622799099997,167.41678100600006,-17.73763299799998,168.3157348850001
+Shefa,Shefa,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Shefa, Vanuatu",-17.73763299799998,168.3157348850001,-17.73763299799998,168.3157348850001
+Shefa,Tafea,227.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Shefa, Vanuatu",-19.54447261799993,169.2801722150001,-17.73763299799998,168.3157348850001
+Tafea,Torba,657.0,36.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Torba, Vanuatu","Tafea, Vanuatu",-13.873643999999956,167.5495830000001,-19.54447261799993,169.2801722150001
+Tafea,Sanma,499.0,28.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Sanma, Vanuatu","Tafea, Vanuatu",-15.513757933999955,167.1799922040001,-19.54447261799993,169.2801722150001
+Tafea,Penama,461.0,26.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Penama, Vanuatu","Tafea, Vanuatu",-15.548909039999955,168.1484143,-19.54447261799993,169.2801722150001
+Tafea,Malampa,431.0,24.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Malampa, Vanuatu","Tafea, Vanuatu",-16.10622799099997,167.41678100600006,-19.54447261799993,169.2801722150001
+Tafea,Shefa,227.0,12.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Shefa, Vanuatu","Tafea, Vanuatu",-17.73763299799998,168.3157348850001,-19.54447261799993,169.2801722150001
+Tafea,Tafea,0.0,0.0,Vanuatu,Vanuatu,Vanuatu,Vanuatu,"Tafea, Vanuatu","Tafea, Vanuatu",-19.54447261799993,169.2801722150001,-19.54447261799993,169.2801722150001
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual.csv
new file mode 100644
index 0000000..8f01e33
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual.csv
@@ -0,0 +1,7 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Malampa, Vanuatu",Vanuatu,260
+Blankets,Oceania,"Penama, Vanuatu",Vanuatu,160
+Blankets,Oceania,"Sanma, Vanuatu",Vanuatu,954
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,2808
+Blankets,Oceania,"Tafea, Vanuatu",Vanuatu,243
+Blankets,Oceania,"Torba, Vanuatu",Vanuatu,319
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_50pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_50pct.csv
new file mode 100644
index 0000000..933a166
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_50pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,6417
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,1540
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_55pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_55pct.csv
new file mode 100644
index 0000000..59eb849
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_55pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,14530
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,3487
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_60pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_60pct.csv
new file mode 100644
index 0000000..a238f51
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_60pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,22103
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,5305
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_65pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_65pct.csv
new file mode 100644
index 0000000..5ce1085
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_65pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,29900
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,7176
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_70pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_70pct.csv
new file mode 100644
index 0000000..c7c822a
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_70pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,47337
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,11361
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_75pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_75pct.csv
new file mode 100644
index 0000000..e39712b
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_75pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,73333
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,17600
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_80pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_80pct.csv
new file mode 100644
index 0000000..23906a7
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_80pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,88012
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,21123
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_85pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_85pct.csv
new file mode 100644
index 0000000..71e7643
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_85pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,114112
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,27387
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_90pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_90pct.csv
new file mode 100644
index 0000000..a5bb6ff
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_90pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,207017
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,49684
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_95pct.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_95pct.csv
new file mode 100644
index 0000000..b4b7e64
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/actual_95pct.csv
@@ -0,0 +1,3 @@
+ItemName,Wharehouse(Region),gglAddress,Country,Total
+Blankets,Oceania,"Shefa, Vanuatu",Vanuatu,308507
+Buckets,Oceania,"Shefa, Vanuatu",Vanuatu,74042
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/rm_data.py b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/rm_data.py
new file mode 100644
index 0000000..db5fb01
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/inventory/rm_data.py
@@ -0,0 +1,13 @@
+import pandas as pd
+
+df=pd.read_csv('actual.csv')
+print(df)
+
+# Drop rows where ItemName is not 'Blankets' or 'Bucket'
+df_filtered = df[df['ItemName'].isin(['Blankets', 'Bucket'])]
+
+# Display filtered DataFrame
+print("\nFiltered DataFrame:")
+print(df_filtered)
+
+df_filtered.to_csv('actual.csv', index=False)
\ No newline at end of file
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/items.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/items.csv
new file mode 100644
index 0000000..b6a1a2f
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/items.csv
@@ -0,0 +1,3 @@
+ItemName,WeightMetricTon,CubicMeters
+Blankets,0.0015,0.01
+Buckets,0.00091,0.00617
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/personsPerItem.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/personsPerItem.csv
new file mode 100644
index 0000000..7599498
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/personsPerItem.csv
@@ -0,0 +1,29 @@
+Item,Disaster Type,Month,gglCountry,PersonsPerItem
+Blankets,DEFAULT,DEFAULT,DEFAULT,0.6
+Buckets,DEFAULT,DEFAULT,DEFAULT,2.5
+Dignity Kits,DEFAULT,DEFAULT,DEFAULT,1
+Family Tents,DEFAULT,DEFAULT,DEFAULT,5
+Hygiene Kits,DEFAULT,DEFAULT,DEFAULT,5
+Kitchen sets,DEFAULT,DEFAULT,DEFAULT,5
+mosquito nets,DEFAULT,DEFAULT,DEFAULT,2.5
+Personal Protection Equipment kit (PPE),DEFAULT,DEFAULT,DEFAULT,1
+Shelter tool kits,DEFAULT,DEFAULT,DEFAULT,5
+Solar Lights,DEFAULT,DEFAULT,DEFAULT,5
+Sleeping mats,DEFAULT,DEFAULT,DEFAULT,5
+Tarpaulins,DEFAULT,DEFAULT,DEFAULT,2.5
+Water Containers,DEFAULT,DEFAULT,DEFAULT,2.5
+Water Purification Tablets,DEFAULT,DEFAULT,DEFAULT,1
+Blankets,DEFAULT,-1,DEFAULT,0.6
+Buckets,DEFAULT,-1,DEFAULT,2.5
+Dignity Kits,DEFAULT,-1,DEFAULT,1
+Family Tents,DEFAULT,-1,DEFAULT,5
+Hygiene Kits,DEFAULT,-1,DEFAULT,5
+Kitchen sets,DEFAULT,-1,DEFAULT,5
+mosquito nets,DEFAULT,-1,DEFAULT,2.5
+Personal Protection Equipment kit (PPE),DEFAULT,-1,DEFAULT,1
+Shelter tool kits,DEFAULT,-1,DEFAULT,5
+Solar Lights,DEFAULT,-1,DEFAULT,5
+Sleeping mats,DEFAULT,-1,DEFAULT,5
+Tarpaulins,DEFAULT,-1,DEFAULT,2.5
+Water Containers,DEFAULT,-1,DEFAULT,2.5
+Water Purification Tablets,DEFAULT,-1,DEFAULT,1
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/province_lookup.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/province_lookup.csv
new file mode 100644
index 0000000..24e7618
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/province_lookup.csv
@@ -0,0 +1,7 @@
+province,warehouse_id
+Torba,Torba
+Sanma,Sanma
+Penama,Penama
+Malampa,Malampa
+Shefa,Shefa
+Tafea,Tafea
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/transportModes.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/transportModes.csv
new file mode 100644
index 0000000..35c098e
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/transportModes.csv
@@ -0,0 +1,3 @@
+Mode,DistanceMethod,BigMCostElim,MaxDrivingTimeCutAboveHrs
+Truck,google,1e9,100
+Air,crowScale,1e9,100
diff --git a/optimization202/ESUPS_case_study/data/vanuatu_simple/transportParameters.csv b/optimization202/ESUPS_case_study/data/vanuatu_simple/transportParameters.csv
new file mode 100644
index 0000000..9bc70ca
--- /dev/null
+++ b/optimization202/ESUPS_case_study/data/vanuatu_simple/transportParameters.csv
@@ -0,0 +1,12 @@
+Attribute,Mode,gglAddress,Number
+FixedAddlTime_Hrs,Truck,DEFAULT,24
+FixedAddlTime_Hrs,Air,DEFAULT,100000000
+StretchTimeFactor,Truck,DEFAULT,1.1
+StretchTimeFactor,Air,DEFAULT,100000000
+SpeedKmPerHr,Air,DEFAULT,0.000001
+FixedAddlCost_USD,Truck,DEFAULT,600
+FixedAddlCost_USD,Air,DEFAULT,1.00E+08
+VarCost_USD_ton_km,Truck,DEFAULT,1.2
+VarCost_USD_ton_km,Air,DEFAULT,1.00E+08
+StretchDistanceFactor,Truck,DEFAULT,1
+StretchDistanceFactor,Air,DEFAULT,1
diff --git a/optimization202/ESUPS_case_study/disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/disaster_prepositioning.ipynb
deleted file mode 100644
index 5602bd8..0000000
--- a/optimization202/ESUPS_case_study/disaster_prepositioning.ipynb
+++ /dev/null
@@ -1,10537 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### api settings for presentation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Case Study: Enhancing Humanitarian Response with ESUPS and the STOCKHOLM Platform"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this comprehensive case study, learners will delve into the strategic and operational efforts of the Emergency Supply Pre-positioning Strategy (ESUPS), a collaborative working group consisting of Member States, NGOs, academics, UN agencies, and regional organizations, all operating within Welthungerhilfe. ESUPS plays a critical role in logistics preparedness and pre-positioning at both strategic and operational levels, aiming to optimize the pre-positioning of disaster relief items, minimize relief response times, and enhance the mapping and analysis of relief stock across global humanitarian organizations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Overview of ESUPS and STOCKHOLM:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ESUPS has developed an innovative platform called STOCKHOLM (STOCK of Humanitarian Organizations Logistics Mapping), which serves as a global central repository for relief stock data. This platform visually maps warehouse and supplies locations and provides an in-depth analysis that helps improve response times and supply coverage across diverse humanitarian crises."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### End-to-End Exploration of the Case Study:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1.\tBusiness Context and Story:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This case study provides an in-depth exploration of the critical challenges ESUPS faces in managing the logistics of disaster relief at a global scale. The case begins by detailing the unique challenges and complexities of humanitarian logistics—such as unpredictable disaster locations, varying relief needs, centralizing and standardizing data, and the coordination of multiple stakeholders. Learners will understand how ESUPS aims to address these challenges through a data-driven approach and the development of an optimized pre-positioning strategy that enhances global preparedness."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.\tOptimization Model and Data Analytics:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A key focus of this case study is the mathematical optimization model embedded within the STOCKHOLM platform. The study introduces learners to the foundational principles of optimization, demonstrating how data analytics can be leveraged to make critical decisions on where and how much relief stock should be pre-positioned. Students will be given a subset of anonymized, real-world data that mimics the inputs used by ESUPS to run their models, allowing them to explore hands-on the complexities involved in developing and implementing optimization models that drive impactful outcomes in humanitarian contexts."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.\tHands-on Learning with a Jupyter Notebook:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To facilitate practical understanding, the case study includes a Jupyter Notebook, which guides learners through the process of building, refining, and implementing an optimization model from scratch. This notebook serves as a step-by-step tutorial, from defining the problem and understanding the constraints to coding the solution and transitioning the model to a production environment. It also includes exploratory data analysis, model validation, and interpretation of the results, offering a holistic view of how data science and optimization techniques converge to solve real-world challenges."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.\tExtending the Problem with Predictive Analytics:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Beyond the current scope, the case study provides avenues for learners to extend their exploration using predictive analytics techniques. For example, students could be tasked with predicting future demand for specific relief items based on historical data, seasonal patterns, or the nature of past disasters. This section encourages critical thinking about how predictive models could be integrated into STOCKHOLM to improve the forecasting and allocation of resources, further optimizing response strategies and minimizing waste."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.\tImpact and Implementation Summary:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final section of the case study highlights the tangible impact of STOCKHOLM’s optimization capabilities on global humanitarian efforts. It outlines the real-world benefits observed in reducing response times, lowering costs, and improving the geographical coverage of relief supplies. Additionally, it provides a roadmap for implementation, detailing the steps required to scale the optimization model, handle larger datasets, and integrate advanced analytics tools. Learners will gain insights into the practical challenges of transitioning from a prototype model to a fully operational tool used by humanitarian organizations worldwide."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Positioning and Relevance:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This case study is intended to offer a unique blend of strategic thinking, technical know-how, and real-world impact. It prepares learners not only to understand the complexities of humanitarian logistics but also to build and deploy data-driven solutions that have the potential to save lives and streamline global response efforts in the face of natural disasters.\n",
-    "\n",
-    "This case study is ideal for students and professionals interested in operations research, data science, in optimization for social good. It provides a practical, end-to-end learning experience that encompasses business strategy, mathematical modeling, data analytics, and the use of technology to solve some of the most pressing global challenges facing the world today."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### A Background on The Crisis that Prompted This Work: Disaster Relief & Mobilization Efforts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In 2015, Kathmandu, Nepal was devastated by a 7.8 magnitude earthquake that claimed approximately 9,000 lives and injured thousands more. The full extent of the tragedy became evident hours later, as over 600,000 buildings lay in ruins, including the nation’s central disaster relief supplies. Despite being only the 31st most affected area, Kathmandu housed around 90% of Nepal’s disaster relief resources, which were now buried under the debris.\n",
-    "\n",
-    "The international community quickly mobilized, with the UN raising nearly $330 million within two weeks to assist Nepal. While this rapid response showcased global solidarity and compassion, it also underscored a critical issue: the aid came after the disaster had struck. In disaster scenarios, the first 24 hours are crucial for rescue operations, as chances of finding survivors diminish significantly after that period. No amount of money can turn back those critical hours.\n",
-    "\n",
-    "International responses, though well-intentioned, often face coordination challenges and logistical delays. The complexity of involving multiple organizations can lead to miscommunication and inefficiencies, making it difficult to ascertain what aid is coming and in what quantities. Studies indicate that roughly 60-80% of all disaster response costs are logistics-related, including stock pre-positioning and deployment. It’s crucial to know the location and availability of supplies to optimize response efforts."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction: "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The Origin of ESUPS "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename='images/ESUPS-Logo.png')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Enter ESUPS (Emergency Supply Pre-positioning Strategy). Founded in 2016, ESUPS emerged from the recognition that effective disaster response requires more than just post-disaster fundraising—it necessitates proactive, strategic planning and resource allocation. Initially launched as a collaborative effort among several NGOs, ESUPS aimed to tackle the very issues that hampered the Nepal earthquake response by creating a global database of pre-positioned supplies. This innovative initiative ensures that the nearest available resources are utilized first, enabling aid organizations to make informed decisions and address the most urgent needs swiftly.\n",
-    "\n",
-    "ESUPS started as a modest project, built on a simple spreadsheet that cataloged available disaster relief supplies across various regions. However, as the project gained traction and more organizations began contributing data, it became clear that a more sophisticated approach was needed. In response, ESUPS partnered with academic institutions like Penn State University and MIT, as well as industry leaders such as Gurobi, to develop advanced models for optimizing the allocation and movement of these supplies.\n",
-    "\n",
-    "One of ESUPS’ early successes was the development of a simulation model that could identify the optimal locations for pre-positioning disaster relief supplies. This model proved instrumental in reducing response times and ensuring that critical resources reached disaster zones faster and more efficiently. Over time, ESUPS expanded its database to include hundreds of NGOs, each contributing data that made the system more robust and comprehensive. The impact of this collaborative effort was soon evident, as ESUPS’ models helped improve response times in several subsequent disaster scenarios.\n",
-    "\n",
-    "By 2020, ESUPS had grown into a globally recognized initiative, with its strategies being adopted by numerous international aid organizations. The project’s achievements include not only the development of cutting-edge optimization models but also the successful implementation of these models in real-world disaster scenarios. ESUPS has been credited with significantly enhancing the effectiveness of disaster relief efforts, saving lives, and reducing the costs associated with disaster response logistics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ESUPS Today"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Today, ESUPS continues to lead the way in disaster preparedness, leveraging the latest technological advancements and fostering collaboration between NGOs, academia, and the private sector. Its success story is a testament to the power of data-driven decision-making and the importance of pre-positioning strategies in disaster relief. As ESUPS looks to the future, its focus remains on refining its models, expanding its global reach, and continuing to make a tangible difference in the lives of those affected by disasters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Diving into the Case Study & Jupyter Notebook"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this notebook, we will take you through the end-to-end process of how ESUPS transformed from a simple idea into a global initiative that is reshaping disaster response logistics. We will delve into the crucial role that optimization plays in this transformation, demonstrating how ESUPS utilizes advanced algorithms to optimize the allocation and movement of disaster relief supplies.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Starting with the initial challenges faced by disaster relief organizations and the inefficiencies of traditional response methods, we will explore how ESUPS identified these gaps and developed a pioneering approach to pre-positioning resources. \n",
-    "\n",
-    "The notebook will provide a detailed walkthrough of the optimization models that underpin ESUPS, offering a high-level explanation of the algorithms used and their practical applications. You’ll see how ESUPS has integrated real-world data into these models to make informed decisions that improve response times and potientally save lives.\n",
-    "\n",
-    "-    **NOTE:** While ESUPS is operating at a global scale, the case study will focus on building a model for only one country: Madagascar. This reduction in scope allows us to more clearly walk learners through a smaller version of the problem, and leverage either our limited-size Gurobi license (available to all learners), or our full Gurobi academic license (available to all currently affiliated students, instructors, and researchers) to solve the model. \n",
-    "\n",
-    "\n",
-    "To deepen your understanding, the notebook includes practical exercises that allow you to engage directly with the concepts. You’ll encounter self-assessment multiple-choice questions and hands-on code implementation problems designed to reinforce your learning and give you the opportunity to experiment with optimization techniques. These exercises are crafted to help you build confidence in applying the concepts without the pressure of graded responses.\n",
-    "\n",
-    "Additionally, for those eager to dive deeper, the notebook features extension prompts that encourage you to think critically about how the underlying optimization problems could be adapted or expanded. These prompts are designed to challenge your creativity and problem-solving skills, enabling you to explore new ideas and potential improvements to the models.\n",
-    "\n",
-    "Finally, we’ll pull back the curtain to reveal the production code used by ESUPS, written by experts in the field. You’ll have the opportunity to examine real-world code implementations that drive ESUPS’ operations, gaining insight into the practical considerations and best practices for creating fast, reliable, and maintainable code. This section will also explore alternative approaches and the reasoning behind key decisions made in the development process, providing you with a comprehensive understanding of how these algorithms transition from theoretical models to production-ready solutions.\n",
-    "By the end of this notebook, you’ll have a solid grasp of how optimization, rather than machine learning, can be the most effective tool for solving complex logistical challenges, especially in high-stakes environments like disaster relief. You’ll also be equipped with the knowledge and practical skills to apply these techniques in your own projects, potentially making a significant impact in whatever field you choose to tackle.\n",
-    "\n",
-    "The evolution of ESUPS from a spreadsheet repository to a sophisticated data-driven solution highlights the importance of efficient disaster relief logistics. However, while technology has significantly improved our ability to manage and allocate resources, it also presents new challenges. The task of optimizing supply distribution in real-time amidst unpredictable disaster scenarios is not just a logistical issue, but a complex data science problem. So, let's dive into the technical challenges of prepositioning supplies together!\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### A Data Scientist's Perspective"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initially approaching this problem, your first instinct might be to leverage ML tools, after all the problem appears well suited for machine learning: We have an implicit reward function in how well our allocation meets the demand for supplies perhaps discounted by transit time and cost, and we have defined inputs (amount of supply and available warehouses) and outputs (where to store the supplies).\n",
-    "\n",
-    "But just because the inputs and outputs are clear, the program is not easy to implement. Let's look at what solving this with ML tools would look like in practice:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### A Machine Learning Approach"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "When considering machine learning (ML) approaches for complex problems, **neural networks (NNs)** are often one of the first tools that come to mind. This is understandable, as NNs are powerful function approximators capable of learning intricate patterns and relationships from large datasets. Given enough data and model capacity, they can approximate any function to arbitrary precision (a property known as the Universal Approximation Theorem).\n",
-    "\n",
-    "However, for this case study on disaster relief in Madagascar, we face significant constraints. We have reliable data on only **64 disaster events**, with each event characterized by limited features—primarily location, type, and impact. Additionally, our objective involves optimizing **supply allocations** for **13 different items** across **27 warehouses**. The challenge here is substantial: we must map a small, sparse dataset with limited features to a vast solution space, all while contending with a noisy reward function that makes it difficult to gauge success. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Challenges with Sparse Data and Neural Networks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sparse data is a notorious problem in machine learning. With only 64 data points, a neural network would struggle to generalize well, especially given the high-dimensional output space—13 items across 27 warehouses—resulting in 351 decision variables. Now there are several ways to deal with sparse data in ML and you might have your own technique or want to give it a try, and you absolutely should! If you find interesting results, we'd love to hear about them, but for now, let's consider one of the most popular techniques: data interpolation.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "**Data interpolation** attempts to create a probability distribution that closely mirrors real-world events, allowing us to sample synthetic data for training. Ideally, this synthetic data is representative enough that a neural network can learn from it. However, generating such a distribution is often as challenging as the original problem—especially when the relationships are highly complex or unpredictable. For instance, allocating goods based on past data might work, but predicting future disasters’ location, type, and impact introduces an additional layer of complexity.\n",
-    "\n",
-    "Likely the first method that comes to mind when picking an ML approach is neural networks, and for good reason. Neural networks are incredibly powerful tools that have transformed the modern world, they're able to approximate any function to arbitrary precision provided they are large enough and have enough data. However, in this case study, we'll be drilling down to look at disasters in Madagascar, for which we only have reliable data on 64 events. Even worse, each disaster is characterized by limited information, mainly location, type, and impact.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "##### Conceptualizing the Search Space\n",
-    "\n",
-    "Assuming we could synthesize realistic disaster scenarios, we might then train a neural network to predict optimal supply positions. This would involve a straightforward training loop: provide disaster scenarios to the neural network, predict supply allocations, compute the loss (e.g., time or cost to meet demand), and update the model using backpropagation.\n",
-    "\n",
-    "However, consider the **search space** that the neural network must navigate. Each potential supply allocation corresponds to a 13x27 matrix, resulting in **351** output variables. To understand the magnitude of this space, let’s do some Let's do some back-of-the-napkin calculations using the the stars and bars combinatorial formula to see how many potential solutions we might have:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let $S$ be the total number of items for a given supply type and $W$ be the number of warehouses. The formula for number of combinations for a given row is:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} \n",
-    "Combinations &= \\frac{S+W-1!}{(W-1)!(S)!}\n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For example we have 40811 buckets accross all 27 warehouses. So we would get:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned} 1.89\\cdot 10^{93}\n",
-    "\\end{aligned}\n",
-    "$$\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For context, the estimated number of **atoms** in the observable universe is between $10^{78}$ and  $10^{82}$. So for each atom could be associated with between $10^11$ and $10^15$ solutions and we would still have more left over\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can't forget there are 12 more rows. And because the rows are independent, we have the total possible number of combinations of each row multiplied by the combination of each other. Let's say we have the same amount of other items, then we get:\n",
-    "$$\n",
-    "\\begin{aligned} (1.89\\cdot 10^{93})^{13} \\approx 10^{1209}\n",
-    "\\end{aligned}\n",
-    "$$\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is not just a large number; it is a number so vast that it defies comprehension. For comparison, a Googol is $10^{100}$. Our solution space is so large that if we could somehow sample a billion ( 10^9 ) combinations per second, it would still take longer than the age of the universe to explore even a minuscule fraction of this space (see proof below).\n",
-    "\n",
-    "When we train a neural network, it essentially samples a point from this enormous solution space during each iteration and then uses gradient-based methods to find a better point. However, with more possible solutions than there are particles in the universe, the likelihood of sampling a truly optimal solution—or even knowing how close a solution is to the optimum—becomes exceedingly slim. It’s like searching for a single grain of sand on all the beaches in the world, blindfolded, while new grains are constantly being added.\n",
-    "\n",
-    "This illustrates why a brute-force approach or even a gradient-based sampling approach is ineffective for such vast combinatorial spaces. Instead, we need methods that can systematically explore the space and make informed decisions to find the optimal or near-optimal solutions more efficiently."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Proof "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$1$ year  $\\approx 3.154 \\cdot 10^7$ seconds\n",
-    "\n",
-    "$13.8$ billion years = $13.8 \\cdot 10^9$ years = $13.8 \\cdot 10^9 \\cdot 3.154 \\cdot 10^7$ Seconds \n",
-    "\n",
-    "$4.35 \\cdot 10^26$ $\\approx 10^{26}$ Seconds In Universe\n",
-    "\n",
-    "\n",
-    "Fraction Explored = $\\frac{10^{26}}{10^{1209}}$ = $\\frac{1}{10^{1183}}$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Optimization: A Structured Approach to the Solution Space"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After exploring the challenges of using neural networks to navigate the vast solution space, it’s clear that the sheer size of possible allocations makes a purely machine learning approach inefficient for this problem. Neural networks, while powerful, rely on sampling from a solution space inconceivably large and gradually improving through incremental updates. This approach can be highly ineffective when dealing with a combinatorial explosion of possibilities, as we saw with our earlier calculations.\n",
-    "\n",
-    "Instead, when faced with such a **well-defined** problem—where we know the objective function, inputs, outputs, and constraints—a more **structured** approach through optimization becomes not only viable but advantageous. Optimization techniques are specifically designed to handle these kinds of problems efficiently by applying systematic mathematical logic rather than heuristic sampling.\n",
-    "\n",
-    "In this case, our problem is clearly formulated:\n",
-    "\n",
-    "**Objective Function** (What we want to minimize):\n",
-    "\n",
-    "-\tMinimize the travel time or cost required to meet demand by pre-positioning supplies.\n",
-    "\n",
-    "**Constraints** (how we can change the variables): \n",
-    "\n",
-    "-\tEnsure demand is met without exceeding available supplies.\n",
-    "\n",
-    "Optimization frameworks, unlike neural networks, don’t blindly sample solutions from a vast space. Instead, they apply logical methods to systematically explore and reduce the search space, ultimately providing a set of potential optimal solutions. This not only increases efficiency but also provides clarity and insight into why a particular solution is optimal, which is invaluable for decision-making and explanation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### The Simplex Method"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Simplex Method is a powerful algorithm for solving linear programming problems, which involve optimizing a linear objective function subject to linear constraints. Based on geometric principles, it efficiently navigates the feasible region—a polyhedron formed by the intersection of these constraints—to find the optimal solution at one of its vertices or \"corner points.\" In linear programming, the optimal solution is guaranteed to be at a vertex because the objective function improves as we move along the edges of the polyhedron until we can't improve any further.\n",
-    "\n",
-    "To visualize this, imagine a two-dimensional feasible region shaped like a polygon on a graph. Each point within this polygon represents a possible solution that satisfies the constraints. The Simplex Method moves from one vertex to another along the edges of this polygon, evaluating the objective function at each corner point. This process is efficient because it doesn't require checking every possible point within the feasible region."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename='images/Simplex.png') "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For example, if you're trying to maximize your enjoyment by purchasing hotdogs and soda with a limited budget, you'd intuitively spend all your money, fully utilizing your budget constraint. The optimal solution occurs at the point where your spending exactly meets your budget limit—one of the vertices of the feasible region where the constraints are tight or binding.\n",
-    "\n",
-    "In the context of pre-positioning supplies for disaster response, each allocation affects the total transit time. The goal might be to meet demand with the minimum possible transit time, which requires allocating supplies so that the constraints are fully utilized—providing just enough to meet each warehouse's demand without surplus. This means the optimal solution will be at a vertex where the supply and demand constraints are exactly satisfied.\n",
-    "\n",
-    "In transportation problems like ours, with whole-number supplies and demands, we're distributing indivisible units like buckets or hygiene kits. Since the suppliers have integer quantities to offer and the consumers require integer quantities to receive, the shipments must also be in whole numbers to exactly meet these needs. Fractional shipments wouldn't satisfy the exact integer requirements, nor would they make practical sense when items can't be divided.\n",
-    "\n",
-    "Geometrically, because the constraints are defined by integer values, the feasible region is a polyhedron whose vertices correspond to integer solutions. The Simplex Method, by moving along the edges of this integer-defined polyhedron, often arrives at integer solutions naturally. The constraints dictate that the total shipped from each supplier equals their supply and the total received by each consumer equals their demand, both of which are integers. This setup leads to shipment amounts that are integers because only whole units can satisfy both the supply and demand without leaving any fractions unaccounted for.\n",
-    "\n",
-    "This approach offers a clear advantage: instead of searching through a massive number of possible solutions within the feasible region—as might be necessary with other methods—the Simplex Method only needs to evaluate a finite number of vertices, one of which will be the optimal solution. In transportation problems with integer constraints, it often provides optimal integer solutions without the need for additional techniques like the Branch and Bound Method (read more about this in the Math Addendum). This makes the Simplex Method both effective and practical for scenarios where exact quantities are essential."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### An Advancing field"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Optimization has taken a backseat with the rise of Machine Learning and Big Data. But if you think back to your courses, you’ll remember that both fields share many of the same mathematical roots. Many core ML concepts, like gradient descent, evolved from numerical optimization techniques like Newton’s method. I bring this up because, even though optimization might seem less flashy compared to the latest ML algorithms, it’s experiencing a resurgence. Recent advancements in solvers and algorithms have made optimization more powerful and easier to use, allowing it to tackle complex, large-scale problems that were previously out of reach. There are also many free educational resources available to get problem solvers up and running with optimization, making it more accessible than ever before—check out www.gurobi.com/learn for just some of the educational materials openly available to learners. All of this means it’s becoming an essential tool for decision-making in various industries, from logistics to finance.\n",
-    "Just as an example, Gurobi, the most popular optimization model currently avalible, has seen a speed-up of 80% for problem like pre-positioning and has increased the solver's flexibility ~67% in the past 10 years \n",
-    "\n",
-    "\n",
-    "Just as an example, Gurobi, the most popular optimization solver currently available, has seen a speed-up of 80% for problems like pre-positioning and has increased the solver's flexibility ~67% in the past 10 years\n",
-    "\n",
-    "And this is still very much under active development. Take a look at some of the improved speeds for large problems in the table below! (It's not important that you know the specific names, but nearly every problem you would use optimization for falls in this table!)\n",
-    "\n",
-    "\n",
-    "|Problem Class | Speed-Up in Past year | \n",
-    "| -------- | ------- |\n",
-    "| Mixed-Integer Linear Program (MILP)  | 12.4% |\n",
-    "| Mixed-Integer Quadratic Program (MIQP) | 22.8% |\n",
-    "| Mixed Integer with Quadratic Constraints Program (MIQCP) | 18.2% |\n",
-    "\n",
-    "<sup> Comparing Version 10.0 (released Nov 22) and Version 11.0 (released Nov 23) </sup>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If your curious about what this means for real life applications, take a look below at some of the examples!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### A Few Real World Examples:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 1.\tMixed-Integer Linear Program (MILP):\n",
-    "\n",
-    "MILPs are widely used in logistics and supply chain optimization. A real-world example is vehicle routing for delivery companies like UPS or FedEx, where the goal is to minimize the total distance traveled or delivery time while meeting constraints such as vehicle capacity, delivery time windows, and route restrictions. Advanced MILP solvers allow these companies to dynamically adjust routes in real-time, respond to traffic conditions, and make last-minute changes. This type of optimization is also critical in energy management, such as optimizing the scheduling of power generation plants to meet fluctuating demand while minimizing costs and adhering to regulatory and operational constraints.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.\tMixed-Integer Quadratic Program (MIQP): \n",
-    "\n",
-    "MIQPs find applications in financial portfolio optimization, where the goal is to maximize returns while minimizing risk. In this context, the quadratic term represents the risk (variance or covariance of asset returns), and the integer constraints can represent decisions like “buy or don’t buy” a particular asset or “fully divest from one sector.” Hedge funds and asset management firms use MIQP to optimize asset allocation, considering various market scenarios and investment constraints. The ability to handle complex quadratic relationships between variables makes MIQP suitable for any industry where risk management and trade-offs are involved, such as in optimizing communication networks and antenna placements."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 3.\tMixed Integer with Quadratic Constraints Program (MIQCP):\n",
-    "\n",
-    "MIQCPs are used in engineering design and manufacturing. An example is optimizing the layout of a factory floor or the design of an aircraft wing. The quadratic constraints might represent aerodynamic properties or stress limits, while integer variables can represent decisions like the number of machines or parts used. In the pharmaceutical industry, MIQCP can help optimize drug formulation processes by considering a wide range of constraints like stability, release rates, and production costs. The combination of integer and quadratic constraints allows for highly tailored solutions that can significantly reduce costs and improve performance in complex industrial systems."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "####\n",
-    "In addition, optimization programs like Gurobi offer powerful tools such as pre-solve, which can significantly simplify models before they are solved. For instance, the country-level ESUPS model we’re working on would typically require 812 iterations of the simplex algorithm to reach a solution. However, with pre-solve enabled, it reduces that number to just one iteration! This kind of optimization not only saves time but also dramatically reduces computational costs.\n",
-    "\n",
-    "Imagine the impact if large language models (LLMs) like ChatGPT could suddenly use 23% fewer computational resources to train and run, or if their size could be reduced by two orders of magnitude while maintaining the same level of performance. This would save hundreds of millions of dollars and lead to a surge in locally hosted LLMs. Similarly, in the field of optimization, problems that were once considered too large or too complex to solve are becoming increasingly feasible each year.\n",
-    "\n",
-    "Just like machine learning algorithms, the mathematical foundations behind optimization techniques can be quite intricate and certainly justify the rigor of PhD programs. However, as with popular libraries like PyTorch, TensorFlow, and XGBoost, you don’t need to master all the underlying math to use these tools effectively—it’s more important to know how to set up the problem correctly.\n",
-    "\n",
-    "For the rest of this notebook, we’ll focus on how to formulate optimization problems and use accessible tools like Gurobi to solve them. If you’re interested in diving deeper into the mathematical concepts, check out the Addendum section. However, this is not necessary for understanding how to use this notebook or Gurobi effectively!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. The Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our first step in the journey is reading and cleaning the data. We won't focus too much on this step because the really interesting stuff happens once we have the data loaded. However, we will showcase how the data needs to be formatted for Gurobi. If this is your first pass of the case study, feel free to skip over this section and return to it later when you want a more in-depth overview of formatting the data.\n",
-    "\n",
-    "Before we begin, I wanted to make a quick note about a line of code you'll see repeated throughout the notebook: %%script false --no-raise-error\n",
-    "\n",
-    "\n",
-    "- **NOTE:** This is just cell magic (if you're unfamiliar, think of it as using the cmd line) telling the notebook not to run this cell. We'll use it in various places to demonstrate ideas or code snippets that are not meant to produce an output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.1 The Environment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are 4 main libraries we'll be using to solve this problem. The other import statements can be explicitly seen in setup_imports.py, but is primarily for getting all relevant methods from the source repo into our namespace:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "Requirement already satisfied: numpy<2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.26.4)\n",
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-     ]
-    }
-   ],
-   "source": [
-    "!pip install -r requirements.txt              \n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import math \n",
-    "\n",
-    "\n",
-    "import gurobipy as gp\n",
-    "from gurobipy import GRB\n",
-    "\n",
-    "\n",
-    "#now hidden code\n",
-    "from setup_imports import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next and final step in this section is to define what data we'll be using to implement the solver, and in this case, we'll be using real-world data from Madagascar. Madagascar is the fourth largest island in the world and is located off the southeastern coast of Africa. Known for its unique biodiversity, approximately 90% of its wildlife is found nowhere else on Earth. The island's diverse ecosystems range from rainforests to deserts, making it a hotspot for biological research and conservation efforts.\n",
-    "\n",
-    "However, Madagascar is also prone to natural disasters, including cyclones, floods, and droughts, all of which have a significant impact on its population and infrastructure. These disasters pose challenges for disaster response and resource allocation, making it an ideal case study for optimization and data-driven decision-making."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Set Up Our Data\n",
-    "COUNTRY = \"madagascar\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.2 Reading and Cleaning \n",
-    "Now we'll load the data into RAM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run optimization\n",
-    "warnings.filterwarnings('ignore')\n",
-    "reader = CsvProblemReader()\n",
-    "dataset = reader.read(DATA_DIR / COUNTRY)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "While we won't be focusing on reading in the data, it's always useful to see the general structure. Feel free to skip this part on your first high-level pass and come back to it later when you better understand the details.\n",
-    "\n",
-    "You can see in the code below all the factors that go into our model. While intimidating at first glance, this dataset class is a way to tie several related lists and dictionaries to the same name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%script false --no-raise-error\n",
-    "\n",
-    "#The class is as follows\n",
-    "@dataclass(frozen=True)\n",
-    "class Dataset:\n",
-    "    depots: list[Depot]\n",
-    "    disasters: list[Disaster]\n",
-    "    disaster_locations: list[DisasterLocation]\n",
-    "    probabilities: dict[Disaster, float]\n",
-    "    items: list[Item]\n",
-    "    transport_modes: list[TransportMode]\n",
-    "    inventory: dict[Tuple[Depot, Item], int]\n",
-    "    inventory_scenarios: dict[str, dict[Tuple[Depot, Item], int]]\n",
-    "    distance: DistanceMatrix\n",
-    "    people_affected: dict[Tuple[DisasterImpact, Item], float]\n",
-    "    persons_per_item_general: dict[Tuple[DisasterImpact, Item], float]\n",
-    "    persons_per_item_monthly: dict[Tuple[DisasterImpact, Item], float]\n",
-    "    disaster_affected_totals: dict[str, int]\n",
-    "\n",
-    "    _zero_demand_threshold = 1e6\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, the dataset fields are mainly data structures holding other object. Now we won't go into all these here, but it can be useful to see how they are set up. Let's take a look at one such object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%script false --no-raise-error\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class Item:\n",
-    "    id: str = field(hash=True)\n",
-    "    weight: float = field(repr=False)  # Metric tons\n",
-    "    volume: float = field(repr=False)  # Cubic metres"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, it's a simple data structure, and most of the code here is actually more about organization and readability, which is incredibly useful in production code. Credits here go to Ben Kennerley, who has been working with ESUPS to enhance STOCKHOLM."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.3 Data Exploration and Visualization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's always useful to see the general structure of data and the broader context of the problem. Now that we've cleaned and loaded it, let's take some time to understand it. The goal here should be to get comfortable with the problem as a whole and give you an intuitive understanding of what we are solving. So, the emphasis isn't on the code. For this reason, most of the following cells have been written as functions to collapse more easily across different platforms. Your goal shouldn't be to understand the libraries used to map, but instead on how Madagascar looks at a high level."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1.3.1 Where is Everything?\n",
-    "One of the first things we'll look at is where the supplies are relative to disasters right now. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
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-    "def graph_locations():\n",
-    "    h = 0.1\n",
-    "    l = -0.1\n",
-    "    warehouses = [['warehouses',\n",
-    "                   depot.latitude + np.random.uniform(low=l, high=h),\n",
-    "                   depot.longitude + np.random.uniform(low=l, high=h)] \n",
-    "                   for depot in dataset.depots]\n",
-    "\n",
-    "    disasters = [['disasters',\n",
-    "                  disaster_locations.latitude + np.random.uniform(low=l, high=h),\n",
-    "                  disaster_locations.longitude + np.random.uniform(low=l, high=h)] \n",
-    "                  for disaster_locations in dataset.disaster_locations]\n",
-    "\n",
-    "    dfw = pd.DataFrame(warehouses)\n",
-    "    dfw.columns = ['Type', 'Lat', 'Long']\n",
-    "\n",
-    "    dfd = pd.DataFrame(disasters)\n",
-    "    dfd.columns = ['Type', 'Lat', 'Long']\n",
-    "\n",
-    "    df_combined = pd.concat([dfw[['Lat', 'Long', 'Type']], dfd[['Lat', 'Long', 'Type']]], ignore_index=True)\n",
-    "\n",
-    "    fig = px.scatter_mapbox(df_combined, \n",
-    "                            lat=\"Lat\", \n",
-    "                            lon=\"Long\", \n",
-    "                            color=\"Type\",\n",
-    "                            color_discrete_sequence=px.colors.qualitative.Safe,\n",
-    "                            zoom=4.5)\n",
-    "    \n",
-    "    # Use a minimalist map style to reduce clutter\n",
-    "    fig.update_layout(mapbox_style=\"open-street-map\")\n",
-    "\n",
-    "    # Optionally center the map around the mean latitude and longitude of your points\n",
-    "    mean_lat = df_combined['Lat'].mean()\n",
-    "    mean_long = df_combined['Long'].mean()\n",
-    "    fig.update_layout(mapbox_center={\"lat\": mean_lat, \"lon\": mean_long})\n",
-    "    fig.update_layout(margin={\"r\": 0, \"t\": 0, \"l\": 0, \"b\": 0})\n",
-    "\n",
-    "    fig.show()\n",
-    "\n",
-    "graph_locations()\n"
-   ]
-  },
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-    "We can see that the warehouses are aligned closely with the most prominent disaster sights*, so assuming they are built to a level that can survive and protect against those disasters, Madagascar should be in a strong position in terms of coverage. However, there's more than meets the eye. Let's dive a little deeper!\n",
-    "\n",
-    "Hopefully, now that you've gotten a sense of the country and the layout, we'll turn off some of the more detailed parts (such as roads and urbanization) so it's simpler to see what’s going on.\n",
-    "\n",
-    "- **NOTE:** locations of warehouses have been modified for this case study for safety."
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-   "metadata": {},
-   "source": [
-    "#### 1.3.2 What Supplies is Available?"
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-    "An important question to ask at this point is: what do we have inside the warehouses? It's great that there seems to be good coverage that ensures a warehouse is nearby for any disaster, but if one of them is full of kitchen sets and there's a flood, they might not be as immediately useful as buckets or tarpaulins. So, let's look at the overall breakdown of supplies by warehouse/location:"
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-           "line": {
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-           "baxis": {
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-            "ticks": ""
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-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
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-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
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-           "zerolinecolor": "white",
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-          "yaxis": {
-           "automargin": true,
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-        },
-        "title": {
-         "text": "Distribution of Supplies for Disaster Relief",
-         "x": 0.5,
-         "xanchor": "center",
-         "y": 0.95,
-         "yanchor": "top"
-        },
-        "xaxis": {
-         "tickangle": -45,
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-          "size": 10
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-          "text": "Supplies"
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-        "yaxis": {
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-          "size": 10
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-          "text": "Quantity"
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-         "type": "log"
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-      }
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-     "metadata": {},
-     "output_type": "display_data"
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-   "source": [
-    "import plotly.graph_objects as go\n",
-    "\n",
-    "# Sample Data\n",
-    "def make_barchart():\n",
-    "    items = {}\n",
-    "    for x in dataset.inventory:\n",
-    "        if x[1].id in items:\n",
-    "            items[x[1].id] += dataset.inventory[x]\n",
-    "        else:\n",
-    "            items[x[1].id] = dataset.inventory[x]\n",
-    "\n",
-    "    items = dict(sorted(items.items(), key=lambda item: item[1], reverse=True))\n",
-    "\n",
-    "    # Extract keys and values from the dictionary\n",
-    "    categories = list(items.keys())\n",
-    "    values = list(items.values())\n",
-    "\n",
-    "    # Normalize the values for color scale\n",
-    "    normalized_values = np.array(values) / max(values)\n",
-    "\n",
-    "    # Define a color scale (e.g., 'Viridis', 'Cividis', 'Plasma', etc.)\n",
-    "    colorscale = 'Cividis'\n",
-    "\n",
-    "    def format_number(num):\n",
-    "        if num >= 1_000_000:\n",
-    "            return f'{num/1_000_000:.1f}M'  # Format as millions\n",
-    "        elif num >= 1_000:\n",
-    "            return f'{num/1_000:.0f}k'  # Format as thousands\n",
-    "        else:\n",
-    "            return str(num)  # Show the number as is if below 1,000\n",
-    "\n",
-    "    # Create formatted text for display\n",
-    "    formatted_text = [format_number(v) for v in values]  # Use the custom formatting function\n",
-    "\n",
-    "    # Create a bar chart with a color continuum\n",
-    "    fig = go.Figure(\n",
-    "        data=[\n",
-    "            go.Bar(\n",
-    "                x=categories,\n",
-    "                y=values,\n",
-    "                marker=dict(\n",
-    "                    color=normalized_values,  # Use normalized values for the color scale\n",
-    "                    colorscale=colorscale,  # Apply the chosen color scale\n",
-    "                    showscale=False  # Hide the color scale bar\n",
-    "                    \n",
-    "                ),\n",
-    "                texttemplate='%{text}',  # Explicitly set text template\n",
-    "                text=formatted_text,  # Display formatted text with 'k' and 'M' suffixes\n",
-    "                hovertemplate='<b>%{x}</b><br>Quantity: %{y}<extra></extra>',  # Show exact hover info\n",
-    "                textposition='auto',  # Automatically position the text\n",
-    "            )\n",
-    "        ]\n",
-    "    )\n",
-    "\n",
-    "    # Update layout\n",
-    "    fig.update_layout(\n",
-    "        title={\n",
-    "            'text': \"Distribution of Supplies for Disaster Relief\",\n",
-    "            'y': 0.95,\n",
-    "            'x': 0.5,\n",
-    "            'xanchor': 'center',\n",
-    "            'yanchor': 'top'\n",
-    "        },\n",
-    "        xaxis_title=\"Supplies\",\n",
-    "        yaxis_title=\"Quantity\",\n",
-    "        yaxis_type=\"log\",  # Logarithmic scale for better visualization\n",
-    "        xaxis=dict(tickangle=-45),  # Rotate x-axis labels for better readability\n",
-    "        plot_bgcolor='white',  # Set background color to white for better contrast\n",
-    "        xaxis_tickfont_size=10,  # Font size for x-axis labels\n",
-    "        yaxis_tickfont_size=10,  # Font size for y-axis labels\n",
-    "        margin=dict(l=40, r=40, t=80, b=100),  # Adjust margins\n",
-    "    )\n",
-    "\n",
-    "    # Add annotations to highlight key insights\n",
-    "    fig.add_annotation(\n",
-    "        x=categories[0],  # Example: Highlighting the highest value category\n",
-    "        y=values[0],\n",
-    "        text=\"Highest Quantity\",\n",
-    "        showarrow=True,\n",
-    "        arrowhead=2,\n",
-    "        ax=-40,\n",
-    "        ay=-40\n",
-    "    )\n",
-    "\n",
-    "    # Show the plot\n",
-    "    fig.show()\n",
-    "\n",
-    "make_barchart()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see from above, the supplies skew heavily towards buckets, water containers, and mosquito nets, which makes sense for an island nation. But while it's great that we have a lot of buckets, it won't do us too much good if none of them are at the coast, for instance.\n",
-    "\n",
-    "- **NOTE:** From here out in the case study, we're going to focus on just buckets as they're the most prevalent item and, for the purposes of this case, it's time consuming and repetitive to analyze all 15 item types of supplies available. \n",
-    "\n",
-    "As you'll see later in the notebook, it's a simple matter to apply the analysis of any one of the supply items to all 15 of them."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1.3.4 Where are the Supplies?\n",
-    "\n",
-    "Let's take a look at the following map to get a better picture of where everything is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
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-    "def graph_supplies():\n",
-    "  supplies=[[x[0].latitude,\n",
-    "             x[0].longitude,\n",
-    "             dataset.inventory[x]]\n",
-    "             for x in dataset.inventory if x[1].id=='Buckets']\n",
-    " \n",
-    "  df=pd.DataFrame(supplies)\n",
-    "  df.columns = ['Lat', 'Long', 'Buckets']\n",
-    "\n",
-    "  fig = px.scatter_mapbox(df, \n",
-    "                        lat=\"Lat\", \n",
-    "                        lon=\"Long\", \n",
-    "                        size=\"Buckets\",\n",
-    "                        #size_max=10,  # Maximum size of the marker\n",
-    "                        #size_min=5,\n",
-    "                        color_discrete_sequence=px.colors.qualitative.Safe,\n",
-    "                        zoom=4.5,  # Adjust zoom level as needed\n",
-    "                        )\n",
-    "\n",
-    "  # Optionally center the map around the mean latitude and longitude of your points\n",
-    "  # Use a minimalist map style to reduce clutter\n",
-    "  fig.update_layout(mapbox_style=\"carto-positron\")\n",
-    "\n",
-    "  mean_lat = df['Lat'].mean()\n",
-    "  mean_long = df['Long'].mean()\n",
-    "  fig.update_layout(mapbox_center={\"lat\": mean_lat, \"lon\": mean_long})\n",
-    "  fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
-    "\n",
-    "  fig.show()\n",
-    "graph_supplies()"
-   ]
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-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the supplies are largely located on the eastern coast line of the country"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1.3.5 Not All Disasters Are Built the Same"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
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-    "def graph_impacts():\n",
-    "    \n",
-    "    # Example values for l and h\n",
-    "    l = -0.1\n",
-    "    h = 0.1\n",
-    "\n",
-    "    # Sample data preparation\n",
-    "    disasters = [[disasters.type.id,\n",
-    "                disaster_locations.latitude,\n",
-    "                disaster_locations.longitude,\n",
-    "                dataset.disaster_affected_totals[key]] \n",
-    "                for disasters, disaster_locations, key in zip(dataset.disasters, dataset.disaster_locations, dataset.disaster_affected_totals)]\n",
-    "\n",
-    "    df = pd.DataFrame(disasters)\n",
-    "    df.columns = ['Type', 'Lat', 'Long', 'People Impacted']\n",
-    "\n",
-    "    fig = px.scatter_mapbox(df, \n",
-    "                            lat=\"Lat\", \n",
-    "                            lon=\"Long\", \n",
-    "                            color=\"Type\", \n",
-    "                            size=\"People Impacted\",\n",
-    "                            #size_max=10,  # Maximum size of the marker\n",
-    "                            #size_min=5,\n",
-    "                            zoom=4.5,  # Adjust zoom level as needed\n",
-    "                            color_discrete_sequence=px.colors.qualitative.Safe,\n",
-    "                            )\n",
-    "\n",
-    "    # Optionally center the map around the mean latitude and longitude of your points\n",
-    "    # Use a minimalist map style to reduce clutter\n",
-    "    fig.update_layout(mapbox_style=\"carto-positron\")\n",
-    "\n",
-    "    mean_lat = df['Lat'].mean()\n",
-    "    mean_long = df['Long'].mean()\n",
-    "    fig.update_layout(mapbox_center={\"lat\": mean_lat, \"lon\": mean_long})\n",
-    "    fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
-    "\n",
-    "    fig.show()\n",
-    "\n",
-    "graph_impacts()"
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-    "#### 1.3.6 How Do Disasters and Supplies Compare in Scale?"
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-    "You can see small blue circles within the red circles, representing supply quantities. The size of each red circle indicates the estimated number of people needing supplies in a disaster-affected area. Meanwhile, the blue circles show the total number of items available, adjusted by how many people each item can serve. For example, one large bucket is estimated to meet the needs of 2.5 people, so the blue circles display the quantity of supplies as $2.5 \\cdot Supplies$\n"
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-   "execution_count": 10,
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-    "def graph_overlap():\n",
-    "  supplies=[['supplies',\n",
-    "             x[0].latitude,\n",
-    "             x[0].longitude,\n",
-    "             dataset.inventory[x]*2.5] #this is people per bucket\n",
-    "             for x in dataset.inventory if x[1].id=='Buckets']\n",
-    " \n",
-    "  supplies=pd.DataFrame(supplies)\n",
-    "  supplies.columns = ['Type','Lat', 'Long', 'Scale']\n",
-    "\n",
-    "\n",
-    "  # Sample data preparation\n",
-    "  disasters = [['disasters',\n",
-    "                disaster_locations.latitude,\n",
-    "                disaster_locations.longitude,\n",
-    "                dataset.disaster_affected_totals[key]] \n",
-    "                for disasters, disaster_locations, key in zip(dataset.disasters, dataset.disaster_locations, dataset.disaster_affected_totals)]\n",
-    "\n",
-    "  disasters = pd.DataFrame(disasters)\n",
-    "  \n",
-    "  \n",
-    "  disasters.columns = ['Type', 'Lat', 'Long', 'Scale']\n",
-    "\n",
-    "  df_combined = pd.concat([supplies[['Type','Lat', 'Long', 'Scale']],\n",
-    "                           disasters[['Type','Lat', 'Long', 'Scale']]],\n",
-    "                           ignore_index=True)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "  fig = px.scatter_mapbox(df_combined, \n",
-    "                        lat=\"Lat\", \n",
-    "                        lon=\"Long\", \n",
-    "                        size=\"Scale\",\n",
-    "                        color='Type',\n",
-    "                        size_max=40,  # Maximum size of the marker\n",
-    "                        #size_min=5,\n",
-    "                        zoom=4.5,  # Adjust zoom level as needed\n",
-    "                        color_discrete_sequence=px.colors.qualitative.Safe,\n",
-    "                        )\n",
-    "\n",
-    "  # Optionally center the map around the mean latitude and longitude of your points\n",
-    "  # Use a minimalist map style to reduce clutter\n",
-    "  fig.update_layout(mapbox_style=\"carto-positron\")\n",
-    "\n",
-    "  mean_lat = df_combined['Lat'].mean()\n",
-    "  mean_long = df_combined['Long'].mean()\n",
-    "  fig.update_layout(mapbox_center={\"lat\": mean_lat, \"lon\": mean_long})\n",
-    "  fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
-    "\n",
-    "  fig.show()\n",
-    "\n",
-    "graph_overlap()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.4 How Does ESUPS Communicate this?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Effective communication is vital in disaster response, not only for coordinating logistics but also for ensuring that the right information reaches the right people at the right time. In this section, we will explore the ESUPS website, which serves as a central hub for sharing critical data and insights with a diverse audience, including NGOs, government agencies, and the public.\n",
-    "\n",
-    "The ESUPS STOCKHOLM platform is designed to translate complex optimization models and logistical data into intuitive, actionable information. Through a combination of interactive charts, maps, and data visualizations, the platform provides users (comprised of NGOs and humanitarian/relief organizations) with a clear understanding of where pre-positioned supplies are located, how they can be accessed, and the impact of various logistical decisions.\n",
-    "\n",
-    "We will examine how ESUPS effectively communicates information about stock and analysis of results through STOCKHOLM, highlighting key features such as:\n",
-    "•\tReal-time Data Visualizations: How ESUPS uses dynamic maps and graphs to show the distribution of resources and potential gaps in coverage. These are updated daily with the newest information available\n",
-    "•\tUser-Friendly Interface: The strategies employed to make the website accessible and informative for both technical and non-technical users.\n",
-    "•\tImpact Metrics: How ESUPS showcases the tangible benefits of its optimization strategies, including improved response times and resource utilization. We'll take a special look at these later in the case to help us understand what our optimization techniques have achieved.\n",
-    "\n",
-    "And finally, in one of the most relevant features of this case study, ESUPS has given us permission to share some of the difficulties they have experienced in their attempts to convey the scale of this problem and their findings along with what they hope to change and improve for their users going forward.\n",
-    "\n",
-    "Let's take a look!\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ESUPS's platform  [Stockholm](https://www.esups-stockholm.org/#/private/signin), which is their platform for hosting this model (you can apply for access by visiting the homepage linked above), has two main pages, the first is the context page, and explains the context of the problem that we've been discussing in the past few sections, along with additional details about their mission and collaborating organizations. We'll look at this one first!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**The Real-time Data Visualization** : Progessive Improvement"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We're going to take a slight detour here to look at how all this information is comunicated in real life! You can skip this section without concern for missing important information. \n",
-    "\n",
-    "As we've seen, getting an intuitive understanding of disasters and humanitarian supplies can be tricky when working with a new country. In fact, it's one of the main challenges ESUPS has faced in driving adoption and explaining why their work is so important. \n",
-    "\n",
-    "Data Visaulization isn't an easy task, but let's take a look at how ESUPS started and how they're continuing to improve!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename='images/Old_Stockholm.png')#,width=575, height=325)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The earlier version of the ESUPS dashboard, while not perfect, played a critical role in enabling the organization to quickly deliver a functional product that demonstrated tangible results to other non-profits. This initial version provided the essential tools needed to map stockpiles and showcase the value of the platform in real-world scenarios. By prioritizing functionality and speed, ESUPS was able to meet the immediate needs of its partners, proving the concept and gaining crucial buy-in from stakeholders.\n",
-    "\n",
-    "The design, though basic, allowed for rapid deployment and iteration, proving that in the early stages of development, it's more important to have a working solution than to strive for perfection. The dashboard's ability to deliver key insights swiftly and effectively earned it significant merit, as it laid the groundwork for future improvements and set the stage for more advanced iterations. This approach underscores a vital lesson in development: sometimes, a good solution delivered quickly can be more valuable than a perfect one delivered too late.\n",
-    "\n",
-    "The initial success provided a strong foundation upon which ESUPS could build. The insights gained from this deployment informed the development of a new version, which addresses the limitations of the first while enhancing user experience and data communication. The new dashboard takes these lessons to heart, incorporating more advanced features and a more intuitive design, ensuring that ESUPS continues to meet the needs of its partners more effectively. Now, let’s explore how this new version improves upon the original and further advances ESUPS's mission."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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yZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+Co+GT4gAAAAcaURPVAAAAAIAAAAAAAACigAAACgAAAKKAAACigADCw7xY9FmAABAAElEQVR4AezdBbwV1fbA8WVhP7FbAQMV9CkGJY1YKCAqDRIqIo0gKCEhDYK0qIBKIyUCgoRiYDz1/+wAExExUB8GBv+9RvfcOXPn9Jx7uef+th8807PnO3tO3Fmz9l47d+7cLRQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDISoG9CBDKyvPKQSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4AgQIERDQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgiwUIEMrik8uhIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBAgBBtAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBLBYgQCiLTy6HhgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAUK0AQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEsliAAKEsPrkcGgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACBAjRBhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyGIBAoSy+ORyaAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIECBEG0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIIsFCBDK4pPLoSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggQIAQbQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSwWIEAoi08uh4YAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAFCtAEEEEAAAQQQQAABBBBAAAEEEAgU+Pzzz+XDDz+UrVu3yo8//ii7du2SQw89VI466ig59dRTpVSpUrLvvvsGrstEBBBAAAEEEEAAAQQQQAABBBBAAAEEENhzBAgQ2nPOBTVBAAEEEMiAwO+//y5r166Rgw86WCpUrCh77713BvbCJhFAIBWB//3vf1K9WlV31RIlTpM5c+e649kwwHvQ32cRh2xozRxDYRH47bffZOHChbJy5UrZsGGDbNu2Leah77XXXlKuXDmpUaOGXHvttU7AUMwVmIkAAggggAACCCCAAAIIIIAAAggggAAC+SIQaoDQX3/9JX/++WfMA9E/HvJ0YUyiQjszkfajONp+tB2lW/z722effTISOODfT7R6a9CC1oGCQLYIJNr299tvv4wdst7guvaa2u6NrTPOPFPmzZsfyntIxirNhrNGwH8N8D6f+9RqgNClFSu4M0477TR5fOEid7ygD/Ae9PcZxKFgt+Rff/1VRo4YIe+//54MGTJUTjr55LQPSAPG/vOfV+WjDz+Sb7/7VnT8mGOOkRNPPEnKly8vBx10UNr70A3s3r1btmzZIh999JHz7+edO+W000+T008/XYoXLyFFihQJZT9BG8mEW9B+wpy2efNmmTRpkkyfPl1+/vnnlDddtWpVufnmm6Vu3bopb4MVEUAAAQQQQAABBBBAAAEEEEAAAQQQQCB8gVADhO67b7TMMH9MjFcOO+ww5w/LJ590snk9SUqYP85eVquWZPImcbw6MT//BRJtP0X239/cPDhRnPZz0klSpUoVKWueWE22+PfX7Y47pFmz5sluJu7y/v3EWuFIk6bfHpem669br54cffTRsVZhHgJ7rECibV8D4w4x3VRoVxX/Mv9OOOEEKV36XDn33HPl7HPOSesm4dKlS6Vvn94RRhMnTZYKFXICEiJmMoJAiAK9775bli17wt3iscceKytWPpWRYFR3JwVsINsDhHgP+rtB4lDALkxPdTWwpkf37rJ58yZn6vwFj8sZZ5zhWSK5QQ2aGTfuflm0aJFosE5QOfDAA53fhh07dnK6sApaJt40DdCcP2+eTJw4QX744YfAxfc23z9q164tXbp0lcMPPzxwmVQnhu2Waj0SXe+PP/6Q/v37y+jRoxNdJaHlKleuLL1795aKJoMjBQEEEEAAAQQQQAABBBBAAAEEEEAAAQTyXyBfAoSCDvskE+jRoWNHqVXrcjI7BAEVgmmJBhMEUZQtW1Y6dupk0tmXDpodOM2/vz0hQMhfUQ2GatigobRs1Sr0Gxf+fTGOQNgC/mssle1rxrDqpruKBuY6uPDCC5PexKJFC6X/PfdErDd+/AS5tFKliGmMIBC2gAa+1KxRXfRmuLfQ/rwaItkeIMR70N/nG4fIdl8QxjR7zJQpk2XmY4+JBo/Ykk6A0P+98Yb06dNHPvvsU7u5mK9FTdDOwIEDpVKlyjGX88/873//K/cOGuRkPPLPCxrXAOX27TvIjQ0apP07NBNuQXUOc9ratWula9eu8uGHH4a52YhtdTdBZvf4vo9FLMAIAggggAACCCCAAAIIIIAAAggggAACCOSJwB4TIGSPVrNGDDB/CC5RooSdxGshEQgjmKBXr7ukQcOGCYn597cnBgjZA9FuFiaadP/nn3+BncRrARXYtWuXvPTSRrn44kvkgAMOKKBHkVi1/ddYYmtFX0q7B+vTp6+cd9550RfyzdHgjKuvvkq+/eYbZ45mPZhruhjTrp4oiQsUpnabuErsJR9fsMDc2B6Qa6GaNWvKyFHhZmjItZMCNCHbA4R4D/q7MeJQcC5K7epr6ZIlMnnyZNm+/etcFU81QGjy5Ekm4GiK7DaZfbxFg+FLms/3HSbLz+effead5QxrhtnJk6fIhRddlGte0ISPTRdZjRo1zBWcqdkKS5guDDWT7fvvvSc//fRTrtU7d+4iN7VsmWt6IhMy5ZbIvtNZZsKECdKjR490NpHwupdffrk8/PDDUrRo0YTXYUEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCFcgowFCGsxwZskzI2r8yy+/yNatW2Xrl1/Ktm3bIp5ItQsWN8FBc+bMlf3NH4wphUfAH0wQ1H5+3vmzfP31Nvnqq22BTx9rtpGHHnpY/n3++XHh/PvLqwChoOPSyv6w4wfZsmWLfLHlC9nx/fe56n/MMcc4gQ2Hh9wFQq4dMSEjAs8/95zT1dCzGzbITpNZZO269XLEEUdkZF97ykb911i0tq+fC3qjTv998fnnzmdDtGPQG3y33dZOWrVunXCQz65dv8nq1avlkEMONVkIKiW8XrQ6FKbphbHdhnV+mzVtIm+++WauzenN7lWrnyYr3D8y2R4gpIfJe9DfJxuHfxr9Hvry448/OoFBM2bMCAwMstVOJUDopZdekltvudluwnnVbkR73NlTzj77bNHv71q+/fZbee65DTJs2LCI7sf+9a9/yYLHF4p+F45VtI01adJEPvzgA3cx7a6sS9duUrduHSlSJOe3pWYZGjigf0TWHK3HjEceSSojaSbd3IPI0IBmZxo6dGjcrVevXl2qVasmZcqUcR7iOfLII51z5nxv++ILefvtt+X555+XFStWmN9puYPKvDs43/xGmz9/vtOdrHc6wwgggAACCCCAAAIIIIAAAggggAACCCCQNwIZDRCKF3ChTxPPnTtXpk97WL73BURoFhjNBkMpPAL+YIJ47UfT4M+YPt0JuvAqHX30MbJk6VLRrDuxSrL7i7WtWPNS2c/69etl/Lj75aOPPorYdNly5WTSpMkEOESoFIyRRg0byLvvvutWtjAGCMW7pi2O3lzSG3crli+XdevXyV9//mlnua9XXHGlDDE3tfbaay93GgPhCxTGdhuG4qZNm6T+dfXcTV1yySXy8ssvu+N33NFdmjZr5o4X5oHCECBUmM8vx77nC7z22mvyyCMzRANCNQuOt2hQ7qEmOMcbuJ5sgJAG7dSvXz8iO5C+/2m2HhsY5N2nDut7aMcO7Z3AeTuvXr3rpF+cLqr837m167CZs2bLKaecYjcT8arHe8cd3eQZ873blpPNsosXLxE99lgl026x9h3GPA0M0gChaEUf1Olouv9uZbo5juYXtO4Ckz1vksl6unHjxqDZzjQNElpuvuNpNicKAggggAACCCCAAAIIIIAAAggggAACCOStQL4GCNlD1ewRnTt3kpd8f0icaAIhKlSoYBfjNcsF/H/UTzSY4L7Ro2XGjOkROg9MfVD0hmyskur+Ym0zaF6q+/nLdMHQuVMnefbZZyI2O+WBqVK2bNmIaYzs+QKFMdAi1bbvPZtfmYxzEydNdLIaeKfr8K23tpXb2rXzT2Y8RIHC2G7D4Bs9apRzw91ua/z4CdK+/e12VE4//XQnG4Y7oRAPECBUiE8+h75HCNw7aJDJ6DIvV12OP/4EGTxkiDz44FQneMgukGyA0P1jx5pupR6yq4sGu0+Z8oA7Hm1As2rWufYaN9vs3iZgZ9GixXLqqadGW0WuveaaiAyjffv2k+tMcFKs8t1330m9enVNJs8d7mJz5s6Ts846yx0PGsi0W9A+w5r22GOPme9Qt0bdXNOmTaV///5y3HHHRV0m3oxp06aZh316BXblputqd2MLFy6MtxnmI4AAAggggAACCCCAAAIIIIAAAggggEDIAntEgJAe0zfbt0v96+tH/HG2du1rZNC994Z8yGxuTxVINZhAA2muNTcQtGsiWzp16iwtzROvsUqq+4u1zaB56exnx47v5frrr3euD7vtzl26yk033WRHeS0gAoUx0CKdtu8/rStXrpQB/e+Rn3/+OWLW2PvHSZUqVSKmMRKeQGFst+nq/fHHH1LrspqiN521aFcsq59eIw1NFrEP3n/f3fxjM2dJ6dKl3fHCOkCAUGE98xz3niLgD3TRLrmaN2/hdOWpWWRuv71dygFCen1XrVLZDfLRrDwaYFTCdCedSPHX7eZbbjH1aR+4qnZ3VbnSpbJ7925nvnbjumbtuoQyDfqDmPr06Wt+l14fuB870V+3MN3sPjLx+s4770g5E6T1Z0B2Rt3fuHHjnKxBYex78+bNTiDSCy+8ELi5Hj16SL9+/QLnMREBBBBAAAEEEEAAAQQQQAABBBBAAAEEMiOwxwQI6eHNnTNHhgwZ7B5pyZJnydx5uZ9odRdgIKsE0gkm6NWrp9MdkQWpZZ5KHT58hB0NfE1nf4EbjDIx3f2MHDlCHnv0UXfrl19+hQwbPtwdZ6BgCBTGQIt0277/zGr3J+1NlyO7TVCgLWeccYbMm78goRuAdh1eExcojO02cZ3gJdeuXSNdu3RxZzYxmRi6d+9hMt3NkPtGj3Kn681nvQld2AsBQoW9BXD8+S1gA10OP/xw0S6eGzZsJEWLFnWrlU6A0Jo1a6Rb15z3w6rVqsmYMWPdbccb2LZtm9S++iq367PiJrBIswgFlY0bX5S2nqw4iWYq0m2tXrXKvE/f4W623nWmO7N+97jjQQOZdAvaX1jTrjFZltauXRu4Oe0e7Morrwycl+pEfZCjQYMGTpdiQdtYZewrVqwYNItpCCCAAAIIIIAAAggggAACCCCAAAIIIJABgT0qQOitt96Spk0au4dZpEgR2fjSy7L33nu70xjIXoF0ggmmTJkskyZOdHHq1K1rUuMPcMeDBtLZX9D2ok1Ldz9Lly6Vvn16u5s/5ZRTZekTT7jjDBQMgcIYaJFu2w86s9NNlxVjxtwXMWvU6PukRo0aEdMYCUegMLbbdOU6dugQ0TXk7Dlz5eyzz3YywdW6vJb89U/WhkMOOUSeXrNWDjjggHR3WaDXJ0CoQJ8+Kp8FAhrUeNBBB8nFF18imuHHX9IJEBphAtpnznzM3eQtt9wq7W7P6W7RnRFjoHGjhqJZb2x5cvkKOfHEE+2o++oP8knkt4Bd2f8btOZll8nIkTkBnXY572sm3bz7CXP4UfPAQdu2bQM3OXPmTKlrfj/FKhMmTDDdxT0csYhmTnrOBHDHKhokpF2KBWUSqlSpkmiWSAoCCCCAAAIIIIAAAggggAACCCCAAAII5I3AHhUgpF3HVKxQ3k0NrwSLFi+R4sWLx9TYtWuXvPHGG/LSxo3y0aaPZMeOHfLDDz/IQeYPlvo07MmnnGJSqZeXSy65xPkDeKyNffXVV/Lhhx+4ixx5xJFyTqlS7ni8gf/85z+mC5ydzmIHHXiQXHjRRfFWceZrF2vvvveuu6wGgZx66qnueLSBr7/+2jwFukZeffVV2W628T+TXr9YseJyxplnSMmSJeXss86W444/PtrquaZruvkXXnjenX7QQQfLhRde6I7v2vWbPPnkk/Luu+/Kt9986/yBvuKll0rZsmXdZVIdSCeYoI8JoHnCBNLY0qFjR2nduo0dDXxNZ3+BG4wyMd39vPTSS3LrLTe7Wz/uuONk5VOr3PFEBsJsJ/HaiNbn22+/dc7He++9J3pN/fDjD861qHWvUKGCVK5cRf71r38lUvWYy4R57Xt3FO8YE7kOntuwQXab/2wZNmxYRDd4gwcPkUP/daid7b5ecEEZ0cCBbCjptv0gA+2+6ZratWXr1i/d2WXMe9TDD09zx70D8c6ld9mg4R9//NG5cfXpJ5/IF1u+kC+//FL2NTdQjzDdRh111FFyYZkLpVLlyk77Dlo/aJoew4svviCffvKpbNmyxfmnx3OouSaKFSvm/CtVqnTEe2/QdqJNS6fOmW63eflelMh1Gs0w1en6We4NAjrttNPk8YWL3M3d3u42ef75nM/YgYPuFc3mkExJpE3rzfTly5+Uzz79TLZ+tVUOPfRQKXlmSTmz5JlykflecvLJpySzS3fZTLTdRAKEPvjgA9m27Su3Htp1kF4jqZTXXntNdu78n7vq+edf4Pi4EzwD6VxLdjOJnC+7bNBrGHUI2m6y09KtR7IOiSyfqXbut9m0aZO5bp8TDSLRrgO/N//226+IHHX0UXL00UdLGfM5UM1kxknmszvM90J/fcMeTydAqEeP7rLqqafcKmkGTM2EmUy503RD9dRTOQEkmoFIMxH5y/vvvycNbrzRnXzuuefKo4/NdMdjDSxevEju8XR1pb8h9LdEOiUdt3T2G2vdMmXKyPueri7tstrNl3b3Fa/cc889MmJEZIZWDS7T36DxinY3pr8BtCs4f0kkOMm/DuMIIIAAAggggAACCCCAAAIIIIAAAgggkJrAHhUgpIdQvlxZ+eWXX9yjmW665NCbN0FFl5sze7ZMnzFdfjBBQfHKQQcfLM2bN5dmzZrLwWY4qPj/uKxPqOqTqokUDXCqWqWyaNCCFs2AtOG552X//fePu/qECeNl6gMPuMuNGXu/VK1a1R33D2gAxtixY5wgjN27c4IQ/MvtZbIv3dzmZml7220JZWKKdaNOb2gPGjjQuZnt3U8iafi9y0cbTieYQG8I6LmzZeKkyc4foe140Gs6+wvaXrRp6e5n1qyZMtwEl9hSoUJFmThpkh2N+ZqJdhKrjezcuVOGmm4CV6xYIXojOVrRmwm3tr1NmjRpIvvuu2+0xaJOz8S1791ZrGNM9Dq46MIyMQ28+/MOz5w1K+Ub397t7AnD6bb9aMcwd+5cGTL4Xnf2fvvt57zXBmViiXUu3Q0EDHzxxRfy2GOPypLFiyM+kwIWFX2f1a6irjNdksQqeoN9wYL5zueW3hyOV2rWrCk9e/YyN6GPjreoMz+MOmeq3eb1e1Gi12lCsEksNM1kVtDPZls6deosLVu1sqPOe2Ovnne64xpE/NBDkdkY3JlRBmK16U8//VR6332XvPnmm1HW/vu7SXdzI/iGG3JupEdd+J8ZmWy7sY7H1mu5CUy+665edtQJyNPsS0HZTtyFAgY0gLxmjerue/Nhphul1aufdr6veRcP41qy20vk+Oyy3tcw6+DdbrLDYdUjWYdYy2eqnfttnnnmGRk/7n7z4MCH/lm5xvVzqNsddzjdc+Wa6ZmQifdCz+YzMphOoMstN7eRl19+2a3XlAemJv1Qgf83Uvv2HaTNzTmB83bjv/32m3N92wAU/f21/plnRTPcxCt9+/aRpUuWuIvF+x3mLhhjIB23GJtNedY80213y5Ytc62v3XtpN1+JlHQChHT700wWyPbt2+falf7m1QdQKAgggAACCCCAAAIIIIAAAggggAACCCCQeYE9KkBIM41cYbrf8JalTyyTU0wGIH/RzCSdO3V0spP458Ub16CfCRMnOVkagpbVOmhdbIlWBzvfvq5Zs0a6de1iR51X3Y/+4TVeada0iXtDr4j5g/azz26I2u3If0y2oM6dOwU+gRltP5qVZMjQoaIZXGKVaDdkNDNS21tvkd9//z3X6vkdIPT666+bbEGt3G5bzjJZk2bPmSN77bVXrrp6J2QqeMG7Dx1Odz/9+vV1ghTsdps2ayZ33NHdjkZ9zVQ7idZGPjZPBmu71Bt3iRa9Nu4bM8bcnI0fRGe3mclr3+4j2jEmcx1kKtDC1rEgvKbb9qMdo2aI02BMb3DklCkPSNly5XKtEu1c5lrQM+Htt9+Sm83Nx59NwFuipXPnLnJTwI03u75mP+jXt6/8+uuvdlJCr5qRYsKEifLv88+PuXxYdc5Eu83r96JkrtOYqCnMrHPtNe57oAaOrVz5lBx77LHulvQGdvXq1WTn//7OYKOfU9plZDIZfaK16Q0bnjUBZT3dbbs7jTJQy3T3opnM4gVpZrrtRjseb7U1G1RN042gBirZkuj3K7u8vs413w2GmCBWW5o0bSrdu0dmzQjrWrL7SOT47LL2New62O0m+xpmPZJ1iLZ8ptq510b3fafJfOPN9uWdH224efMW0rVbt2izJVPvhVF3GNKMdAJdevXqKSuWL3dr0rtPH7n++hvc8UQG5syZbYLPh7iL1q59jQy6NydI2J1hBiaaLrAeeGCKO+nGBg1McOHd7njQgAZUtmjR3P0dUbxECVloMr/F+x0RtC3vtHTcvNsJa1i7D1u9enWuzWlgjgboJFLSDRDSfWiXsBtN1l9/0YyppUunlhnOvy3GEUAAAQQQQAABBBBAAAEEEEAAAQQQQCC6wB4VIKR/9O/geapQnwx//oUXcwXKPP/cc9KtW9dcN1o1Q5B2oVHyrJImEOZ4+eTjj+WDD96Xjz76yM3qYykOO+wwmT59hugfgf1lsMlMMc9kqLClV6+7pEHDhnY06muf3nfLE+ZGn7ckEsyhT7pWMTe7/zLde2m5tFIlGT9+gncz7vCzzz5rgkO6yS5zk9EWvbmn3aCdddZZcsIJJ5puaz6R981xv2u6GPHeQNfu1pYsfSJm105BN2SmPviQ1K1zbcSNuaJmW7+ZG92aySU/A4Q0UODGG643XY9ssxwybvx4qVSpsjsebSBTwQv+/aWzH33SvH7962TH99+7m03k+DLZToLayEOme6eGDW50A+s0Q9CZ5lo8++yznZvjH370oWhXIJ+Ytrn7r7/cY9GBmpddJiNHjoqYFm0k09e+3W/QMSZ7HWQi0MLWr6C8ptP24x3jDea6/9B0O2TLrbe2ldvatbOj7mvQufR2+eQu+M+AZopoYwIO9b3FFs1MVLVqNSdYVbuF2/rlVtGsGps3b3Jedbl4AUJ6w1JvXHqLdrN3+hlnmGvlTDn++BPk888+cwJFvdnQdHm9jmbOmh01C1yYdQ673eb1e1Gy16n3fKQ7/MYbr8tNLVq4m9FuTR+Y+qA7bgf6my5aFi1aaEeltcny16FDB3c83kBQmx42fIQ0bdLY+V6kN7WLFy8hpUqXktNOO122mLaq3YK+8+477vcMu4++/e6Jm/kqk21X6xF0PEHX6DAT5Dx79ixbdYkVJOAu5BtoarLWvfVWTnalhYsWSwnP98AwryW760SPzy6fiTrYbSfzGnY9knUIWj6T7dza6AMC7UzWTX1/9xa9rjSQT9+P9XfGzv/tlM0fb5ZNH22Szz77OzA6VoBQJt8LvfXMxHA6gS4PPfSgjLv/frdaifwuchf+Z2CwCQaaNy/nd1mVKlVk7P3j/Is54/rZ3UC/j27d6ozreetn3nPr1q0XuLx+L+1o3n/tOdTAzvtNfRP5HRG4Qc/EdNw8mwllULsAK1asWK5tVa9ePddv11wLeSaEESC0YMECE5CV81lpN9+7d2/p1SsnU5ydzisCCCCAAAIIIIAAAggggAACCCCAAAIIhCuwRwUIDR06xOl6xR7ixRdfIlMfjLy5ptlJGjduFPGEvKb179K1qzRq1DjwaU/N2KBPni5evMhu2nktV768TJ6c85SpnalPDN/e7jY7aoJ3ov8h2i70lwl6qFataq6uzk477TQJutFl19NXf+ahu80fSIO6/vj888/MH70bRGS1OMPcWNYMAGeYm8z+8tyGDdK7T++I4BLNcKE3sqOVoBsy1cwfjx+cOtXJMqBp/auYp0yLFy/ubELrtHnzx45RtG0mOj2ZYAL1Xrp0iUw2XW15sz1pJgDNCJBISWZ/iWwv2jLp7Kd9+9tFz6Mt0W442/n6mul24m8jGmSnWblsPfVpbc1wpF3s+cv//d//SXcT4ObvXmnGjEfiZkjJi2vf1td/jHodJ3sd+LNtNTPt8r333rW7kKdWrZYjjjjCHbcDGvCnN7SyoaTT9uMdv/+GYR3zZHz//gNyrRZ0LmO9JzcxQRZvv/WWu52q1aqJ3hArWvRwd5p34CXzFPz06dOd7EU33XSTd1bEsA2y0HNbt149k6HoFhPQeULEMnbk0UcfkbEms5a3m77+AwZInTp17SIRr2HWOcx2m9fvRalcpxGQaY7c069fxPeMAQMHybXXXptrq5rhqHWrlu70Y445RlY+tSpqAJi74D8D/jatwWX771/ECcDUQGDNFliuXHn/ak42FM2Kouvbom1QsyTGyiKUybar9fAfT7TvTR+YgEANCLZFg8LXrVufUDeuus7HJmi8Xt06dnWn+1rtxtZbwryW7HYTPT67fCbqYLedzGvY9UjWwb98ptu52mgXwS2aN3MC6rxWGrivny9HHnmkd7I7rJlPJk6cIP8+79+BGYQy/V7oViRDA+kEumg3bZ065gRAakbTaeYzM5nib4sXXXyxPGgeXohWNm3a5JxHbUO26Gd5s6bN5CwT4KVdjm02WS+fffYZmTJ5svzmeehCM0BpoFcYJR23MPbv3cYckz2tdevW3knO8FTz+65x48a5pkebsHDhQlm2bFnEbP2+P9k4JlP0d6T/t0B587v86aefTmYzLIsAAggggAACCCCAAAIIIIAAAggggAACKQjsMQFC+sd17cLKm/Gml0kJrwExtmhQSMOGDeSD99+3k+Skk0+W4ebJ+XPOOcedFm1g2bInZKC5yer9Q7A+gaoBQN6iNwi0+5qff/7Zmaw3obTLr1g30Lw3/E42XaJpJghbVq1+WvQGYLRy76BBMn/+PHe2Bg54uyTRGXrsTU03ZO+8/ba7XM2aNc2NwGGiAVLRigbPXHddPTeoaP/995dlTy6Xo48+OnAV/w0Z7ZJMHdRs5KhRoTxRG7hjM9EfTKABGRebmwC2/GkyLH219SuTseNzkyHpA/fpYJ1/sOmGp2uXrlL/+pybh3a9aK/+/XW74w5p1qx5tMVTnp7Kfr4xT/qOGjVSVqxY4e53b5NRa+7ceYHBYHahvGgn/jZi9603XAbdO9jpOsBOC3r97rvvnJtF2qWDLUHBgHaevubVtW/36T/GMK6DRua9S7N42LLW3NgOChCy87PhNZW2n+hxT33gAdP11nh3cQ1cHDs2J0uBneE/l9GCD3R57b5Is7nZLFf6+bLQ3AxLpAs8/dwICoqz9dAgi/Xr1ztdnSTShcZo8377yCM5wQsaDDp//gK7Ofc1k3XWnaTabvPjvSiM69SFTXJAPydr1qjufm/QrFN6jWs2NX/R7zlXX3WlfPnll+4szRqoQQiJFH+btuucfvrpMnHS5JjfNzTIRrNr2Dau6/bt20+uq1/fbibXa6bart2R/3hiXaP+QIHhI0ZIrVqX203FfNUsJprNxBZ/AFemrqVkji9TdbDHnOhrJuqRjIPW07+8rXum2rlu3/99XL/3d+zUyflumEjgrmZ99AcR5cV7obXJ1Gs6gS5btmxx3u+8dUvm/U67ZWvTpnXE70P9zTdr9hzvJnMNa3e0Pe/s4QROemfqedTPau9vQZ2v0zQ4qGHDRt7F0xpOxy2tHQes3KVLF9P12gO55ujvxEMPPTTX9GgTNGOrP7Bnb5N1qZTJZJtMuf32253gav86O3bsiPm71r884wgggAACCCCAAAIIIIAAAggggAACCCCQvMAeESCkmUHamKcat2//2j0CzUiyaPGSiBuuT69e7XSvZRfSgIkFCx6P6B7Czov2qhlnJk+e5M4+5ZRTZeGiRbmCf7qZjERr1uQ8xfjQQw/LhRdd5K7nHxg1cqRo1gctbU3XBNOnTXO7QIuV+UGXr137avni8891UEqWPEvmzssJFnImmv+tXbvGBMDkZP45+uhjTGaihTG7C7PrTpkyWSZNnGhH5frrbzCZhfq4496BaDdk7unfP2p6fu/66Qz7gwkS2ZYGR7Vu3cbJGpTMH7h12/795VWAUDvzR/Ebb7wx1+F9ve1r2WSeaNYsM/Pnz3eDunRBDVIbaoLBKleO3XVaXrSTaG2kU6fO0rJVq1zHFTRBu/3TLqK8N6m1Kx7NkBRU8vLa1/1HO8Z0roNUAy2CPArKtExeY3NNN5BDTHeQtkTLSuA/l7GCD9atWytdOne2mzTXaQO56+673fF0Braa7k408FNvpCVSvvnmG7nqyivc7jE1uHPjSy/nyi6VyTprPVNtt/n5XpTOdZrIuQlaRjMUagYhW666+monu58d979qd3MaeGNLjRo1ZdTo0XY05qu/TevCetN7umZi+/e/Y66rMzt37iTr161zl7v88itk2PDh7rh/IFNt1+7HfzyxrtHHTdc0AwcOsKuKZgUZM2asOx5tQIM09HqyGQf1+8LTa9ZGZB/K1LWUzPFlqg7RXKJNz0Q9knHQevmX12mZbOefmK6m6tWrG/G9RL8r63fmdEpevBemU79E1k030KWlya73+uuvubsqVqyYzDMBr7GCanXhnTt3yg3X148IptTppUqXlpkzc7ob1GlBZceO7033tw3c6z5oGTtt6LDhcsUVV9jRUF7TdQulEv9spFatWk4WOe829SEMDVxOpvQ3vweH+z4v9P3Uvrcmuq2ZM2fKLbfckmtxzeJ7/vnn55rOBAQQQAABBBBAAAEEEEAAAQQQQAABBBAITyBfA4S2mywpmtp90aKFotlhbNnL3EAdZzL7+J+mb9qkibz1Vk7Wkbp164neCEym6A2HK6+4XH766Sd3tX6m+5h69a5zx3Vg6dKl0td0z2VL6zY3S4cOOSny7XT76g3y0W7RxpobVrauV1xxpQwdNswuGvHqf7L2lltuFQ0g8ZfmpsuD/5rumWwJynxk5/lff/nlF6lRvZqb2UCzGWlWo6ASdENGg5FWrFyZK4gqaP10pvmDCRLdltavfIXyUr58BbnssssSrqd/f3kVIJTocdnlNFhOz7c+NR+v5EU7CWojmjVr4cJFST312/POO2XlypwMSbFuUuflta/GQceY7nWQaqBFvHO+J8/P5DW2Yvly6dWrp3v42s3ifBMw6i/+cxkr+GCu6YJjyJDB7iauvOoqMz7UHc/rAX+befrpNXKUL/tbpuvsr0Oima/y670o3es01XN8U4sW8sYbr7urayafChUquOP+Ae1y6Jratd3JmqlktTm/2kVYvOJv07p8Mm315ZdflltubuPu5rzzzpNHHn3MHQ9jwN9ugtqu3Y//eGJdoxowoJma9HuNFg0S1jYZL0DYf8wNGjY07x932So4r5m6lpI5vkzVIeJAExjJRD2ScdAq+pfXaZls5/369ZUlixfrbpxyZsmSMmfO3ISDOu16/te8eC/07zPs8XQDXTQovMGNN0T81jvrrLPl3sGDRa/3oKIZgPr27RORNdYul0h3u5qBc+yY+xIOXNnHPHTSyHS11bbtbXKIyUoaRknXLYw62G3odyRv1jqd3sJ8bk30PEBil431GlaAkHY5HPQZOWvWLNOdak5XkLHqwjwEEEAAAQQQQAABBBBAAAEEEEAAAQQQSE0gowFClSpVljJlyrg1+2v3X05acg2K0X+aNef3339359uBO3v2lEaNGttR51W7Japerao7rYjJpvDEE8tydcXlLhBjQJ/a16f3bQm6UaRPnVYzT6bbDCex0tlvNllfrjNPHWvRm1Tr1j8jk0ymoocenOpMK2pu+K1duy7wJsPCxx+XAQNygpxmmj+MlipV2lnP/k+7LNAAH1sOK1pU1pkbYolmotD1vDfr9Ansl8wNwqBuc4JuyCSTGcbWMZVXfzBBKtvQ86RPAZ9iAlbiFf/+9rQAoTIXXmiy7Nwo2pVcrG7k7HHmVTsJaiP+rlpsnWK9andb2i5tOfvss2W2uRnnL3l97ev+g44x3evAew3qPhINtNBlC2rJ5DXmf+8MI4PQK6+8LDe3yQmcOPKoo+TxxxdKUfOemx/Fn+ll2vQZcsEFF0RUJdN1TqXd5ud7UbrXaQRugiOaeaRunWvdpTWIa5XpKjTeZ3SLFs3l/954w10v0c+goPenhx6eJheaz4xEin99bedrTDadMEsibdfuz1+fWAFCuo5matKMTbb07XeP6Uo1MsjbzrOvfXrfbb4zPmFHZd68+aIBIN6SqWspmePLVB28x5nIcCbqkYyD1tG/vE7LVDvX3yKXVqwQ0e1UvMyhWp94Ja/eC+PVI935YQS6jDHBOppd1Vv0t1zdunWd7qk0g6oGSr7zzjtOsKUGa9mHR/SBEO81Hy9zmD/YS/epQfb6PaHkWSWd7Kvvv/e+k7FTgwe9v0U1KH+GycbmD8b11jvR4TDcEt1XvOU0+FS7QvWWu+66S+5OMktiWAFCem0E/Va733QF2dpkFaYggAACCCCAAAIIIIAAAggggAACCCCAQOYEMhoglGy19Y+y3bv3cLqM8K/r72IokadH/duw4//973+lebOmdtTJPDPJZDLyF29GAM1qtM50yVG0aO6n+x966EGT8eh+Z3X7dLNmEtD1bZk1a7acU6qUHXVfu3e/Q1avWuWM6006fcpeA3i8ZdWqp6RH9+7upCuvvFKGmO6mkik9enSXVU895a6yaNFiKV6ihDtuB4JuyGiXZ/qH+0wXfzBBq1atpY65ceAtmj1AM099/fU2J8jsyWVPRnRNp8sedNBBMmLkKKlYsaJ31VzD/v0lenM214biTPDvJ87izuzzTDcxw4ePkOOOOy6RxZ1l8qqdBLURzdyiTycnU3bt+k3Kmi7Fdu/e7aymwXUbnns+1yby49oPOsZ0r4NUAi1yYSQ44bfffou40ZnIagceeGBCgWiJbMsu42/7YV5j3vdd3V/16jVk9H332V27r/5zGSv4YMeOHVLrspoRN9Gca9EEHR53/PHuNjM5oF0haVcdn3/2mTxostHpjXpbRo4a7QQM2nF9zXSdU2m3+flelO516rVNdHjs2DEy7eGH3cWbN28hXbt1c8ejDSxYMF8GDRzoztYb2AtMQFq84m/TunyyAYfly5V1s/DEChqOVxfv/GTbrl3XfzyxrlFdRzNPtDBZFW25+OJLRDM3RiuabUgDzG3WodKlz5XHTPc2/pKpaymZ48tUHfzHGm88E/VIxkHr519ep2Wqnb/++uvS8qac7+waPKZBZOmWvHovTLee8dYPI9BFr79OHTuIBuQkU/RBE/090L59TnbV2rWvkUH35nQx6t2ePxBJv9t07NRJGjZslOv3la73wQcfmCCZu+RD82rLGWeeKdOmTU87k1AYbrZO6bxqAFRQoPO9xrCzp1vVRPYRVoCQBisFZcwbbLJKdTLni4IAAggggAACCCCAAAIIIIAAAggggAACmRPYIwKE9jdPkLYyTwu2bNkyMKuNHr4G4OgNYVuuq1/fpJ7vZ0eTevU/0auBSU8uz+nqyG5Mb/jpjT9bNDPNFVdcYUfdVw020qAjLSNGjJTLatUSvVFWtUpl+fHHH53p7Tt0lDaezBQ6UZepVrWK/PDDD84y2s2ZdnfmL/4/dleuXEX+ff6//YvFHN/w7IaI7k/GjR8vmuHJX4JuyDxj1j3ssMP8i4Y+nkowwR9//OEEPt0/7n75autWt07azcziJUvk4IMPdqf5B1LZn38biYz791O2bFnRG5RaNKvWNybgSTNQvPnmmxGb05saIzXQ6dJLI6ZHG8mrduJvI3ubbhk2bnzJXLtFolUt6vSaNWs4x28XeHbDc86T3XZcX/Pj2vcfo9Yj3esglUAL3W8qZfDge2Xe3NzZmGJtK5UsULG2p/P8bT/MAKFRI0fKo48+4lbh+utvkN59+rjjdsB/LuMFH/gDj3Q7muWgYYOGotnm9PMijKLv/++++45z3X/26Wfyucmop91OaXY9bzYD7770/aCm6UbRXzJZ51TabX69F6lLutep3zbeuGa4uPzyWhHvY0HZaYK2o12danZAb1aHRx+bKeee+/fnQ9A6Os3fpvXzWY87mXLtNdfIZ5996q6i3Zsd7eu+zp3pGwiz7eqm/ccT7xrVda6rV082b96kg6IB3JqxKVr9ly17Qnp7smTEyjiUiWsp2ePLRB0cqCT/F3Y9knXwL5/Jdu4/1lhdnibDmFfvhcnUKZVlwwp00YDwWSY4b+z9Y2WXCWSOV/R3ypChQ831vUoG9L/HXbxps2Zyxx05D07YGU8//bTc0a2rHXU+u/UBjXhd9OpnbqeOHeWFF5531611+eVOoL47IYWBsNxS2HXEKuoe1G3aQBOg2rVrjlfESlFGwgoQ0oCxo8yDMf4yYsQIadeunX8y4wgggAACCCCAAAIIIIAAAggggAACCCAQokC+BAhp2vZTTz1VihUr5vyrWfMyOT5Odgbthku7lLEl3W5Eyl5ysZthQwMcXnrp5VzZMz42XYfV+6frMN3vtXXqmO7Acp7212nffPONXGaCHPSPrxogod2L2aCUO3v0kKeeWqmLyYUXXWQCnHIyDOi0d95+Wxo3bqSDThkzZmxg9iR/dxp2+XRee/bq5TxN69+G/4aMBqm8aII/8qKkE0zw4YcfOhkFfv75Z7eqTZo0le7mHEQr6ewv2jaDpie6H20PQ4cNlf+a7Ai2aNdikydPcdqPnRbtNa/aib+NRAuwi1ZP73R/FztBmbby49r3H2MY10EqgRZeq2SGC0OAUKuWN8lrr73mskT7TPCfy3jBBxr80KVLZ3lm/Xp323ZAM61UqFBBGpv3Fn31Z3uzy8V6XblypfO58Oorr4gGiCRTogUIZbLOqbTb/HovCuM6TeZ86LLPPvusdOzQ3l3tpJNPlqlTH5TIPIDu7FwD95ib3htffNGdXv/666VPn77ueNBAsm06aBtNmzSRt97KCUpdtfppOeaYY4IWdadlou3qxlM5nscefdQE0I5w66bBAho0EFRuvfUWeWnjRmfWQSZoWDM1aqbBoJKJaynZ48tEHYKONd60sOuRrEOyywcdT6LtfOjQITJn9mx3E7fe2lZuCyFIIa/eC92KZ2gg7EAX7Zp5isnc+o4JktXupm0mSVt9DaKvf319qVOnrtNV4+TJk2Sy6brZFn2YQh+q8Jcbb7xBPnj/fXdytO8F7gKeAX3IQH/z2Uxj+vm+cOGiwGyrntViDobtFnNncWZqRlL/d44uXbrIoEGD4qwZOTusACENij7rrNzZaadMmSJNm+Zk+Y3cO2MIIIAAAggggAACCCCAAAIIIIAAAgggEIZARgOEWrduY/7YWi+inocfcYQbQBMxI85IN/OE45o1T7tLRbtR6i4QZ6Bu3Tryyccfu0stWfqEE7TkTvhnoHbtq50/Xuuo3jzTm2jeokFLGsCg5dJKlWT8+Anu7CeeeEL69L7bGd93331Fs6N4b0p5n1jWDBXPmgwABxxwgLu+HejcuZOsN92bhVmi/dHcf0NGA7nUJi9KooE00eqy1GQM6ts3J4OIZqbS4Ka9TXaBoJLu/oK2GTQtmf38+uuvojcU/vPqq+6mNKBu/vwFgan43YXMQF61E38biRdw4a2jf7hNm9aigRK2TJs+Qy644AI76rzmx7XvP8YwroNUAi0iIJIYGTJkcEoZhK4xWUXCLMm0/WT2q9dJpUsrRmTamT1nrpx99tm5NuM/l4m219mzZ5kMSPdFzXJQrHhxJ+vdtdfWSShQaKu5+ThwwICIDAW5KvvPBA02PfGkk+QXE/Co3Y3ZEu9zL+w6635Tabf59V4UxnVqrRN97WpusK5duybRxeMud/Ahh5jvOmsDvwvYlVNt03Z9fW3WtElE1rpYAUKZbrupHI92gaXB2TbblnbhqgGm/rJt2zaT+fFy2W0C/7QkEoCly4V5LaVyfGHXQbeXagnLIlmHZJcPOr5E23nPO++UlStzMokOGTJUtMvgdEtevRemW89462cy0EW7Dn7vvffk22+/MVnAjpETTjhBjj322Igq9TeBlIsW5nS/OHPWLClVqnTEMs9t2BDRDZl2C7rcZIeN9hsgYuV/RvyBSNdce60MHJhcAI13u5l08+4nkWH9bq3dqXlLHfPgyyxjmUwJK0BovQnCvvrqq3PteunSpVKjRo1c05mAAAIIIIAAAggggAACCCCAAAIIIIAAAuEJZDRAKMzuZDp16hiR0WG4SUFeq9blKUtcfdWVTlcudgPLlj0p+uS/v+gT6vqkui0LHl8Ykaq+Y4cOJrDnGWe2PvWvN59s+e6775zuQ+yTsWPvHydVqlSxs+Vm0+XYK6+87Iz7g4vchcxAr149ZcXy5e4k/aP3ITG6znIXjDGgT9rXrRsZvKWLh3FDJsZuY85KN5jA33Wc7ky7jovWJVC6+4t5MJ6Zye5HsyDpU9D6VLUtiXR3kVftJMw2Uvvqq+SLL76wh+kE4PmzWOTHtR/mMdqDSyXQwq5bUF+TbfuJHuczzzxjugPp4C5+hAk8XbN2XWCgTjrnUjPEzTY3z+bNm5vryXu787Llykn/e/qLvi9HKxpgcb3JhrDzf/+LWEQDQjWwQbuUKmYy6p1sPoNOOulkJxhVb2qOGDFcZj72mLtOvAAhXTCsOtudptJuC+J7kT3eZF71M77WZTVFu7oMswwcdK/ECtZLp03beiYaOJEXbTfV4/FmadTjCgr0fvjhh+T+sWPtYcus2XPknHPOccdjDYR1LaV6fFq3sOoQ6zgTmRdGPZJ1SHb5oONItJ37A3lGjhotNWvWDNpkUtPy6r0wqUqlsHB+B7rc3u42ef75v7v/0i4FX3xxY64gytGjRskjj8xwj07Pn57HZIr+JtPfZrZo1p2VT62yo0m/5rebt8INGjSQZcuWeSc5D8a88847EdPijYQVIDR69GiTLS/noQ6733fffVdOOeUUO8orAggggAACCCCAAAIIIIAAAggggAACCGRAoMAECPXr11eWLF7sErTv0FHaeP6I685IYEC7Tbj4ogvlzz//dJfe8Nzzcuihh7rjdsD/x2Jv0JOmoa9SpbKTYUJT0a823VYcddRRdlXnVbsQ066jtDRs1Eh69uzlDPszYNx1d28TFHKjM8//v4kTJsgDD0xxJ0frHsxdII2BMG7IpLr7MIIJLjM3BLZv/9qtwrjx46VSpcruuHcgjP15txdtOJX9aNdJrVu1jOh2Idax6L7zqp2E1UY0cO6Siy9yM0BoFi3t6s/fbVN+XPthHaO3TaQSaOFdvyAOp9L2EzlOf9d0Qd0/2u2EcS41aE9vrD2+YIG8//57dtPu6+GHH+5k+dJsX0Gl3W23RWQO0mDUW26+Ra4yT89rdrloJZUAIbutdOtst5NKuy1o70X2WJN91ZvRelM67FLmwgvl4YenRd1sGG060cCJvGi7qR6Pdhum3YfZ0tZcZ23b3mZHnVftMki7i9WiGcY001iyJd1rKdXj89Yz3Tp4t5XOcDr1SNYh2eWDjivRdn6fCVaYMWO6u4mOnTpJq1at3fFUB/LqvTDV+iW6Xn4GuuhvNs0Wpg8BaClZ8iyZO29erqp3aN9eNmx41p1+++3t5eZbct4f3BkxBjQzWVXzu84W/T76/AsvRmR/tfMSec1PN3/9hgwZEtid2EsvvSSlS0dmY/Kv6x0PK0DoWpOdac2ayOx7+oDAx57svt79MowAAggggAACCCCAAAIIIIAAAggggAAC4QkUmAAh/43mWDeE4/HoE/FXmi4nbNGuqDYGBCbofA0i0j8W//TTT87i5ctXkEmTJzvD2q2Idi+i5bx//9s8uZqTaciZaP43aeJEmTLl7+W93Z/ok7D6RKwtT61anSulvp3n7apMp914YwO56+6/uy6zy4T1GsYNmVTr4j/H3mCsRLdZ/7p6smnTJnfxocOGm+5FrnDHvQNh7M+7vWjDqe5n2NChTjcndrvHH3+CLFy0SA488EA7KeI1r9qJv40caYLitEucZMs327ebJ/RruKuVKHGac3zuhH8G/H55ce37jzHRbqn8dfeOpxJo4V2/IA77z10q17T/uDdufFHa3nqrO1mzCTxuMruVKFHCneYdCPtcvvrqK05Gkv/+97/e3cjFF18iUx54IFd3Jv5sR3q9zJ07L1cwacTG/hnRLhO160RbEskgZJf1viZbZ++6qbTb/HovCuM69R57vOHrTBeqmzfnfN5oBsGTTQaoZIt+v9AuR23Rm9JLTRelJ598ip0U8RpGm04kcCKv2m6qx6NBppqFbsuWLY6P9zuWTtDgbA3StuXu3r3lhhuCA7HtMvFeU7mWUj2+aHVJpQ7RtpXO9GTrkaxDsssHHUsi7VzXW7JksfTr29fdRJ26daV//wHueKoDefVemGr9El0vPwNdtJ21aZ0TrBXtARH/+3GvXndJg4YNEz1EZzntsrBs2UvkL88DJI8vXCT62ZJKyU83f303mC7Ygn4P9ezZMzCTj399Ox5GgJBmDi1ZsqTdpPtav35981v6EXecAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIjECBCRBauXKl9Lyzh6tw/vkXyPQZOank3RkJDPj/2Kw3dqc+mHNzzr+JXj3vlBUrVjiTNZhow3PPSZEi+4v35m3nzl3kppYt/avKm2++KXqDwpblK1bKCSecIKNGjpRHH/37j6DRnoa167zxxutyU4sWdlQuvOgiczPxYXc8zIEwbsikWp90gwl27dol5cuVjcgMtWjxEilevHhgldLdX+BGAyamuh89F3WuvcZ9alo33bx5C+narVvAXkTyqp3424hWZr3p8qlo0cMD6xVt4vPmOtKbJ7ZUrlxF7h83zo66r/lx7fuPMYzAg1QCLVyEAjqQatuPdriaQaBxo4aybds2d5FrzFPoAwcOcsf9A5k4l7qP4cOGyaxZMyN2N8cE/px11lkR07R7I+3myJZkMsA1bdJE3nrrTbuqpBogZDeQaJ3t8vqaSrvNr/eiMK5T77HHGvZ/th9kuv1ct2696HeEVIo/uLV16zbSoWPHwE2F0aYTCZzIq7abzvFMNUF5EyaMd528XYgNHTpE5sye7czTwNqnTSDrwWl2z2p3lMy1lM7x2f0FvSZTh6D1w5qWaD2SdUh2+aDjSaSd63oa8Nm8WVN3E+n8xnA3Ygby6r3Qu89MDOdnoEv//vfIooUL3cOK1iW0P7PgjaZLrbvuSu5hCs0Q2MCXzXXV6qedbj/dCiQxkJ9uQdUsVqyYybK6PWKWdqPmfbAiYmbASBgBQgMGDJBh5juUv0ydOtUEdTb2T2YcAQQQQAABBBBAAAEEEEAAAQQQQAABku9eYwAAQABJREFUBEIWKDABQv5sI5o1Yq7pKuLMgCcQ4xn1v8f8sXlRzh+bb2vXznRT0Tbqav4ABc0gVLZsOalevZrs+P57Z73FS5aK/uHVXzQ1vne5Pn36imYZuOH6+vLhhx86i99yy63S7vbb/au64998843UrFHdHT/kkENEMw6FdaPL3bAZCOOGjHd7yQynG0zw1ltvSdMmOX9YPuigg+S551/IldHD1ind/dntxHtNZz/+p8/32Wcfk1VoTmC7z6t24m8jevwPTH1QLrnkkngUEfPbtr1VNr74ojtNA+w00M5f8uPa9x9jGIEH/kCLNWvXyZFHHuk/3KwaT6ft+yF+++03ufnmNvLf//s/d5be9Nen+zXoMlrJxLm0+2pi3m/eNu87ttzTv7/UrVvPjjqvnTp1lGfWr3enaXCnBnnGK5odpWKF8qJd+tiSboCQbieROtv96Wsq7Ta/3ovCuE69xx5reODAAU6Xc3aZeIFqdrlor/7uyrSrlZVPrQr8/AqjTScSOJFXbTed4/n6669NVozL3YwfzZo1F81U9scffzgZ6ux3tLAywnjPX6LXUjrH591f0HCidQhaN8xpidQjWYdklw86nkTaua63c+dO5/3WbmNv811LM72dccYZdlJKr3n1XphS5ZJYKb8CXT7//DOpZ7I56fWs5dxzz5VHH4sMzLWHcWePHvLUUyvtaEoPUyxb9oT09mRo1S5ANcNsrK5A3R0GDOSXW0BVnEndu3eXiSazrb8MHDhQunbt6p8cOJ5ugJAGeJ9zzjmiXW17i36f08xCBxxwgHcywwgggAACCCCAAAIIIIAAAggggAACCCCQAYECEyCkx968ebOIm8Nly5Y1XbpMTYpF/9hct04dN8uMBhotMcE9p5wS3JWHbly7/9BuxrS7MS2axaVa9erS8qa/s/oUMxlqFptMNdHKXXf1kuVPPunMrlmzpvQ0ae+9AT+PzZwlpUuXjra6M71unWvlk08+cZeJlUnGXSiFgTBuyKSwW2eVdIIJ9Ny0bHlTRPu44IIyMm369KjVSWd/UTcaMCOd/WiQwE2mnf3fG2+4Wy5d+lx55NFHA28c50U78bcRrVjFihVlwsRJbh3jDbz77rtO4IFdbr/99pMnnlgmxx1/vJ0U8ZrX177/GMMIPNDsBN5uqRYuWhy1W6yIgy/AI+m0fe9h6/t2N5M564P333cn63v3faPvk6rVqrnTggYycS7tfjRziWYwsaV1m5ulQ4cOdtR59QaD6oR58xfImWeeGbFM0MiTTy6Tu++6K2JWGAFCidTZu9NU221+vBeFcZ16jz3asN7YrGGCdneaLG+2TJ4yRcqVK29Hk37V7Fi1Lqvpfs/QDYwbP14qVaqca1thtOlEAifyqu2mezwdzTX37LPPOE42sOpZk9Wuc+dOrt0jjz4m5513njsexkCi11K6xxerronWIdY2wpiXSD2SdUh2+aDjSKSd2/X8y2rQswY/p1vy4r0w3TrGWz8/Al30+29nDbA117Itsd5n9cEPfQDEFv1eqZ+30bKI2uXsq+6vlQlUf/311+wkKVuunOkmOucz3p2R4EB+uMWqmj5Iob+d/aVIkSLy6quvJtSVWroBQm3btjUZdHN3yX3bbbeZLIkj/VVjHAEEEEAAAQQQQAABBBBAAAEEEEAAAQQyIFCgAoT0j8SdOkbefL1vzBipVi0nu048I293Ybpsrcsvl+HDR8RbTW4xmStefvllZ7kyZcpIhYqXyvhx9zvjrVq1lo6dcm5E+Te2Yvly6dWrpzP56KOPMQFCvaRb178zpRx51FHy9NNrZK+99vKvFjG+ft26iJtd+jTrfP3Dd4kSEcvFG/n000/l1FNPjbpYGDdkom48zoxUgwk0S9Po0aPkMd8fnAcMGCjXmmCwaCXV/UXbXrTp6e5Huzxo1KiRmyFB99PLBJk1aNgw1y7zop3424itxJChw+TKK6+0o1Ff9Xx1Ml3nbNjwrLtMo0aN5c6ef18j7kTPQF5f+/5jDCPwoEeP7rLqqafco4p1o8tdqIAPpNv2NXvOwscfl0mTJ0UEYyhLl67dpIWn68VoVJk4l3Zf/uMbZQKWatSoYWc7r/4sLAMH3SvXXHNNxDL+EQ1K1e4Fv/vuu4hZYQQIJVJn705Tbbf58V4UxnXqPfZow/4sE/o5vtp0Q7O3CVpLp+j3G+/N8Bo1asqo0aNzbTKMNu0PhgjqRiev2m66x+Nvaw89PE3mzJktq1etcuw0C8z8BY/nckx3QqLXUrrHF6ueidYh1jbCmJdIPZJ1SHb5oONIpJ3b9d555x0nw9pu8x3FljDec/3tM1Pf322dM/GaH4Eu4+6/33SnnBOgVfHSS013grmz39jj/d5kdL3qyivkl19+sZOkzIUXOl0yx/uNpSvoe8bQIUPcdXWgnwk4qlfvuohpyYzkh1u8+t10003m9+P8XItVNw++aNbSeEV/R37++ecRi2l20/Ll4wfITps2Tdq3bx+xrh3RbjtLJPmb1q7LKwIIIIAAAggggAACCCCAAAIIIIAAAggkJ1CgAoT06c7WrVrKa6/lPN2pT4h2MMEG2q1ErD8A643mQYMGupl8lOmggw82fxCeGzN7kOWc+dhjMmLEcGf0iCOOcP7o/PTq1c64prvXtPfRyo4dO0wQU1WxNx0aN24is2b9nSJf//Csf4BOpLTSY//Pf9xFTzzxRNEgmES6q/nss8+cIJrvTJYCfZI+Wgnjhky0bceb7r/BpN2E6HmNVfRm6rj7x8pHH30UsZjegNcb8bFKKvuLtb1o88LYz5Ahg02XenPcXRxsuplbYrJWHXX00e40O5DpduJvI3a///rXvxzzKlWq2Em5XrUbjx7d75Dnn3/enafdCSx7crkcZW6yRyt5fe37jzGMwIMxY+6T6ebmiC3X1a8vffv2s6NZ+ZpK29ebe9qN2IsbX3SCg3788ccIG80cdHu726XNzTdHTI82ksy5fHDqVNm1a5e0MDfQ4nXhqJ8pjRs3kk8+/tjZtX7+rFv/jBQtWjSiKv7MGhqgOdt87mgXiEFlx47v5S4TAPjCCznXiF0u6GZ1Jups96ev6bTbvH4vCuM69R57tOE2bVrLq6+84s5u0rSpdO/ewx1PdWDt2jXStcvfwcO6DQ0kWG0CiA8//PCITSbTpiNW9IwkEjiR6bZrq5Pu8WgGwVq1LpNvTXesWlq2auV0/2bfOzT4VINQ45VMXUvJHF+m6hDv2P3zM1GPZBy0Psku7z8GHU+knXvXGzZ0qOnGdZY7Sa9B7QL4pptaxg0AfGnjRtnxww65/PIr3PXtQKbfC+1+MvUaRqDL1q1b5fgoWSK99d616zcZc98Y93eSztPvvDNNttVixYp5F8017M8ipAvUu+46ueOO7lE/0/X75aKFC83DIsMiur3STDuTTfYg/WxPtYThluq+o62ngXAXX3xx4GwNHpowYULgvHQnrlixQq43XWwHlS7mc2/QoEFBs5iGAAIIIIAAAggggAACCCCAAAIIIIAAAhkQKFABQnr827dvlwY33pArs0KFChWldZs2UrJkSTnE/CHZFu224+2335ZRI0eIPvXoLcOGDw/8Q753GTv8xRdfSO2rr7KjUmT//WXXb785ARqaOSDeH5C93bQceOCB7hOuY8aMjdtFjt3pO+Y4mjRpLPrHbFt0vw0bNpIbbrzRCXTSmxm26LFr4Mwz69fLvHlz5Y8//nACmTSgKVoJ44ZMtG3Hm+4PJtCgq9PNk//+olk1tpjzsWXLFtfRu0yJEqfJzFmzRJ1jFf/+EglIirW9aPPC2I/e6NSMIvqEtC2X1aplgtZyp+PPdDvxtxG9ea03aO3N2BtuuNEJsNAANntdaNCDPh081mT88gdzxcvAZY83L699/zGGEXjg7zJKu3S46+67pW7devYQRbteO/LII0W7ycmG4m/7Qdf07r92mxvBP5kbqz/IDtO+PzYBN7Y7R79BUdPWhpobuMl05ZTMuRw7doxMe/hhOcwE+bRs2UoqVKhgbkieKkWK7B9Rlddff13GmQxy3oDN88+/QKbPmBGxnI78x3Tb0bp1q4jp2tVRz5695JxSpdzpGhj14osvyL333usEOmjg3PkXXCAbX3zRXSYoQCgTdXZ3aAbSabd5/V4UxnXqPfag4S9M5oRrrqkd8Tk8a9bsiHMZtF4i0/Qz+rKaNSLe54M+l5Jp09H2m0jgRKbbrq1bGMdz/9ix8vDDDzmb1ODvn00wqpb9zXc1DbLSANZ4JVPXUjLHl6k6xDt2//xM1CMZB61Pssv7j0HHE2nn3vU0iLmO6dL3G/Nbw1s0E03btrc5vzEOO+wwd5YGiurn9kMPPugEdeoybU1XSf6S6fdC//7CHg8j0KVa1SpO95r6HbGcyTbj/a2m9dXvkCtXrnQCg2zgrU7X3zWaOUi7+0qkdDdB6DZ7mF1ev1PpuSlVupTpcqyE6MMlX375pXzwwfsm8GhmRMCnrqPfARaYrGNHBwTh220m8hqGWyL7SXaZwYMHO981gtbLRJDQkiVLTEB1cJDm6aefLv8xD794f78G1YtpCCCAAAIIIIAAAggggAACCCCAAAIIIBCeQIELENJD1z+0dzBdcdinxf0cGphwnHlK9ePNm3MFEumye++zj9zZ487ALpr82/KOX1evnmzevMk7yTwNeYP07tMnYlrQyAMPTJGJvqcyNcjomWeejRvI4t3elCmTZdLE4BT7+sdVzU6hNy82mWP/wWQu8he9QV9QAoT8dU9k/JJLLnGyspx08slxF/cHLwTdiI27kQQWCGs/ixcvknv6RWacGT9+glxaqVKuWmSynQTdtOva7Q7TbcDtbpYsrZDe/Dn99DPkm2+2iwbYBRXNNjRy1GjnZk3QfP+0vLr2g47x8YWL/NVJalxv/terV1c+N9m8vOXQQw+Vk085xQmO0SfsHzM3q0qVKu1dpMAO+9t+qgei7221a18jt7VrJ8cee2xSm0nmXNqb4t4daMYi/Uw58YQTnBuYX3/9tWjwpbfo+652axQtC9aAAf2dbEjedXRYb1rqudfPsk9Nu7BZ5nRe/wEDzM3LD0Sz19kSK0DILqOvYdTZbi/ddpvX70XpXqf2uKO9jh8/TjS7ii2a0WLxkqV2NO1XzVToPedBQU/JtOloFUo0cCKTbdfWLYzj+fzzz+RakznQG0Ct29f3jUEm6C6RkqnrP5njy1QdEjl+7zKZqEcyDlqXZJf31t8OJ9rO7fL6+sYbrzvdoP5gglaDin4G6fv2lyZIXT+zvW0uWoCQbieT74VB9QxzWhiBLlVNgJAGAdui331OPPEk2f+A/WXbtm2y3Xy2+oODNZC6v8mUemUC3dfa7WoXnU3NwxT+h0LsfP0NeID5/aXBXUFFg4dGjBwlVatWDZqd1LQw3JLaYRIL165dW9aZ7quDinY3NsYE9OvnT7plxIgRcs8990TdzNKlS3N1zRp1YWYggAACCCCAAAIIIIAAAggggAACCCCAQCgCBTJASI/8K/NHeX1KVLOSJFOOPvoY6WeCLIKCKuJtx/uEul12wsRJUrFiRTsa9VUDG7Q7Gm+peOmlzlOx3mmJDM+fP09Gmj+4/mYyGCVTipcoIe1uayeaeSZaCeOGTLRtx5ueTjCBBgR169bNdOVWPd5u3Pn+/e3pAUJ6E0ozUXnb/AkmaGHhosWi2Ub8JVPtJFob0T/yazd+mlkrkaKZczS4LtmnhvPi2o92jIkcV6xltAshbafeQBD/8pr9igChv1X0Kf6rr7pamrdoIccdd5yfKqHxZM5l0E3xeDs55ZRTZarJHhErcElvRLZu1dLJNBFve3rzsnPnztK8eQunW0tvsEiiAULx9pFInb3bSLfd5vV7kbfuYQ7/9ddfcuUVlzs3s+12NWjt1lvb2tG0Xz94/3250WRJ9BZ/N6bJtGnvdrzDiQZOZLLt2vqEcTy6rZtNFslXXnnZbtZ5fXjadClTpkzEtGgjmbr+kzm+TNUh2jFHm56JeiTjoPVKdvmgY0m0nfvX1W55u3XrKh+aIM1kSvv2HWJ2f5mp98Jk6pjKsmEEuvgDhOLVo2TJs+Rek+lGM8wkW7SbsodNNkDNCJjMbyX9Pdfjzp7OwxbJ7jNo+TDcgrYbxjQN3L/ssstE23pQ0eCsPuY7eteuXYNmx5223mSv1YyPGzZsiLqszu/QoUPU+cxAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcwIFNgAIcuxZs0amTF9mrz51lsxb7rrDdHrrrtOGpkU59rlRCrl/954Q1q0aO6uqt1YaAYgfdo0XtHgjpo1qkdknrjr7t7mRuCN8VYNnK9P22omoVWrV7ldaQQuaCZq1qBWrVubp2GruV0+RVs2jBsy0bYdb7o/YCfa8vpH61PM09vFihc33f8Uc7LU1KhRI6Hz4N2mf397eoCQ1v2dd975u5s5c6PalhY33SRdugT/AT8T7SRWG9Gb26NGj5KXX3op4ql6W1cNftCbte3a3Z7wTVu7rv81k9d+rGP01yPZcX0f6du3T+DT7freNHvOXClhgvmyofivsaBj0mw32v1PUZP5TLsQ0y49LrigjFx80UVyxplnxn3PCtqmd1oy5/Krr76SJUsWy5PLnjQ3zSK7pPRuU4e1y5nGjRpLNfOk/T6mXccrGlwyf948ExQ63u2Oz7+OHvftJhPXRRdd7MzyZ5MJChDKZJ299Uu33eb1e5G37mENP//883J7u8jug5aZtpJIxrpk6tCwQQN577133VWuq1/fyYxnJyTTpu06/tdkAicy1XZtncI4Ht3WiuXLpVevnnazokHRi0wAbaIlU9dSMseXqTokamCXy0Q9knHQeiS7vK279zWZdu5dT4e13T/55JPyyCMzYgYK6WeYBpbceGMDudQE/u9txmOVTLwXxtpfGPPCCHTRLKrLVywX7aYxWtFuabW7Ts1uU6du3aQDyP3b1QxPY+67z+n+TTMLBRUNUtdsl9o1XNWqVYMWSXlaGG4p7zyBFbVrr7rGWbtujlaONxl5W5gg7Xomi27p0rGzW6rxsmXLTDdxs2Tt2rXRNulMv/POO83nWt+YyzATAQQQQAABBBBAAAEEEEAAAQQQQAABBDIjEGqAUGaqmNhWd5jutF599VXnyf7vv//OSVN/uLnZfOSRRzp/bNYuYrKxaPcv2h3Cpk2bRY/7119/dTJtHH/8CaJ/1NXjPtgEMlEKt0CY7SSRm3ZbTNcbeuNhmwm42PnzTuc61O6ULrmkrOh1GWYpiNf+77//7nRXuHnzx87NMg021Aw55cqV43oNs3GksS3t3mvLli/k66+3m39fy74mCOi4449z3ldPPvkU5701lc1rRpZNmzaZALFP5LNPP5N99t1Hjj/ueCltAjnTDQzLVJ3tcYbRbsN8L7L14jVvBDLZdsM4As3Ocu+gQe6m0gn6zfS15FYyxsCeUAet3p5SjxhUGZ+lnwH/Mb8xtpsuUzWY4sADD3Tet7U74+ImWF0DW5MthfW9UNvTRx9+KN98+43pXtN017mXdrd5rBxrviOeU6qUaGbMTJTt27fLZvPZ+5H59/POnU4A4WkmiFC7i0s2k2Um6pdf23zttdfMgwdNomYS8tZLu1M9//zzna7HjjjiCCeAWz8X9Du/PsDw8suRGdy863qHe/fubYI5e3knMYwAAggggAACCCCAAAIIIIAAAggggAACeSiQNQFCeWjGrhAo1AKJBAgVaiAOHgEEEEAgzwXqX1fPCb7THWumwVWrV0vRouEGpOb5QbFDBBBAIMMC2t1Y27ZtZd26dRndk2ZbnGAySTVr1iyj+2HjCCCAAAIIIIAAAggggAACCCCAAAIIIBBbgACh2D7MRQABnwABQj4QRhFAAAEE8lXglVdelpvbtHHrcMUVV8rQYcPccQYQQAABBGILDB48WO69997YC6U4t7rpjnXo0KFSymSJoiCAAAIIIIAAAggggAA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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename='images/Disaster_Dashboard.png')#,width=575, height=325)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The newer version of the dashboard incorporates several effective design elements that help uninitiated users quickly grasp the context and understand the information presented. Building upon the foundation of the initial version, this iteration features a more logical layout, with distinct sections clearly labeled to make it easier for users to identify and comprehend different data categories at a glance. The use of diverse visualizations—such as a map, pie chart, bar chart, and line graph—enables multiple data points to be visualized simultaneously, offering a comprehensive overview of the situation.\n",
-    "\n",
-    "A key improvement in this version is the effective use of white space. By strategically separating visual elements, the dashboard reduces clutter, ensuring that the information is not overwhelming and making it easier for users to digest. The clear representation of data ensures that each visualization has a specific purpose and is easy to interpret. For example, the Storage Facilities Map now clearly shows the geographical locations of storage facilities with available stock, providing important context. The pie chart uses distinct colors to differentiate between types of disasters, simplifying the interpretation of proportions, while the bar chart allows for a quick comparison of the number of people affected and the number of disasters by region. The line graph displays trends over time, with a dotted trend line to indicate the general direction of these trends.\n",
-    "\n",
-    "The dashboard’s minimalist design elements further contribute to its effectiveness, an improvement from the earlier version. By avoiding unnecessary elements, the design minimizes distractions and enhances readability, ensuring that the focus remains on the data itself. Purposeful use of color now guides the viewer’s attention to key information more effectively. Interactive features, such as buttons labeled “SEE DISASTERS” and “SEE DETAILS,” suggest that users can engage more deeply with specific data points, which enhances both functionality and user engagement.\n",
-    "\n",
-    "Moreover, the consistent color scheme and font style create a unified visual identity, lending a professional appearance and helping users navigate the information more intuitively. Altogether, these elements combine to present a cohesive design that significantly improves user experience and data comprehension compared to the earlier version.\n",
-    "\n",
-    "However, as with any evolving tool, there is always room for further refinement. While this version addresses many of the initial challenges, the ongoing development process continues to identify areas where improvements can be made. By revisiting earlier designs, we can better understand how these refinements come about and how they contribute to a more effective and user-friendly dashboard. Let’s now take a look at one of the earlier pages to explore these opportunities for further enhancement."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename='images/Stock_Dashboard.png')#,width=875, height=325)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As the dashboard continues to evolve, ESUPS is taking the opportunity to further enhance its intuitiveness and user-friendliness. Building on the successes and lessons from earlier iterations, the latest redesign aims to address some of the shortcomings that were identified in the previous versions. By incorporating clear labels, units, and detailed explanations, the new design strives to provide users with the necessary context to interpret data effectively. For instance, sections like “NATIONAL AND COLLECTIVE ASSESSMENT OF STOCK QUANTITIES” and “TIME GAINED BY RE-DISPATCHING ITEMS BETWEEN LOCATIONS” will benefit from added clarity, helping users to understand the information presented more intuitively.\n",
-    "\n",
-    "One of the key areas for improvement in this redesign is the refinement of the color scheme. The use of distinct and contrasting colors will make it easier for users to differentiate between categories in the bar charts. Additionally, clearly indicating what each color signifies, along with providing complete chart titles and fully displayed x-axis labels, will ensure that users can grasp the full context of the data being assessed. These visual enhancements are crucial for making the dashboard more accessible and easier to interpret, especially for users who may be less familiar with the underlying data.\n",
-    "\n",
-    "Another critical improvement is the addition of context and explanations for key metrics. For example, providing details on figures like “39% - 14,615” under “National Stock Level Support” will remove ambiguity and highlight the relevance and importance of these numbers. Establishing clear connections between different data types—such as percentages, quantities, and time in minutes—will create a cohesive narrative that guides users through the data, making it easier to follow and more meaningful.\n",
-    "\n",
-    "To further enhance the user experience, the redesigned dashboard will also focus on improving presentation aspects. Larger text sizes and decluttered visuals will enhance readability, allowing users to quickly grasp the information at a glance. Incorporating interactive elements, such as tooltips, will enable users to explore the data in greater depth and gain additional insights. Simplifying terminology and offering clear explanations for complex concepts, like “Time Gained by Re-Dispatching Items Between Locations,” will further aid in user understanding and ensure that the dashboard is accessible to a wider audience.\n",
-    "\n",
-    "These iterative improvements are a testament to the dynamic nature of data visualization and communication within the industry. By continuously refining and enhancing the dashboard, ESUPS is committed to delivering a tool that not only meets but exceeds user needs. This ongoing process of iteration and enhancement underscores the organization’s dedication to creating a valuable resource for decision-making, ultimately helping ESUPS better serve its partners and fulfill its mission"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Optimizing!\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.1 Introduction to Gurobi Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we get into anything too deep, let’s first take a general look at how optimization works. It's broken down into two main parts: **The objective function** (what we want to solve), and the **constraints** (what limits our input)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The **objective function** tells the program what we want to do, think maximizing profit or minimizing travel time. Let's say in this example, you're buying $x$ hotdogs and $y$ soda cans. You like both equally, and you want as much as possible. So, our **objective function** would look like this:\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$\\text{Maximize} \\hspace{.2cm} \tx\t+\ty $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we obviously would want to just make both x and y infinite, but in the real world that obviously isn't allowed, so we need a way to **constrain** the objective function. Let's say in this example a hotdog cost $3 and a soda costs $1.50 and you don't want to spend more than $20. We would write:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\\begin{aligned}\n",
-    "3x\t+ 1.50y \t&\\leq \\hspace{.2cm} 20 \\\\\n",
-    "x\t \t \t&\\ge \\hspace{.2cm}\t0 \\\\\n",
-    "y\t \t \t&\\ge \\hspace{.2cm}\t0 \\\\\n",
-    "\\end{aligned}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Putting these together we would typically write the final equation as:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\\begin{aligned}\n",
-    "\\text{Maximize} \\space\tx\t+\ty \\\\\n",
-    "3x\t+ 1.50y \t&\\leq \\hspace{.2cm} 20 \\\\\n",
-    "x\t \t \t&\\ge \t0 \\\\\n",
-    "y\t \t \t&\\ge \t0 \\\\\n",
-    "\\end{aligned}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you're intimidated by the math don't worry, the notation is a formal way to ask how much to buy. Just like machine learning algorithms, while the underlying math can be intimidating, actually applying it is much easier as most of that is abstracted away by the API."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So how do we go about solving this? It's easy, there are three steps:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. Instantiate The Solver**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Restricted license - for non-production use only - expires 2025-11-24\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_1= gp.Model() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. Add Our variables**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create variables\n",
-    "x = model_1.addVar(vtype=GRB.INTEGER, name=\"x\")\n",
-    "y = model_1.addVar(vtype=GRB.INTEGER, name=\"y\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. Add Our Constraints**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<gurobi.Constr *Awaiting Model Update*>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_1.addConstr(x >= 0, \"c0\")\n",
-    "model_1.addConstr(y >= 0, \"c0\")\n",
-    "model_1.addConstr(3*x + 1.5*y <= 20, \"c1\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**4. Add Our Objective Function**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set objective\n",
-    "model_1.setObjective(x + y, GRB.MAXIMIZE)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**5. Solve!**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
-      "\n",
-      "CPU model: Apple M1\n",
-      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
-      "\n",
-      "Optimize a model with 3 rows, 2 columns and 4 nonzeros\n",
-      "Model fingerprint: 0x0d7a692e\n",
-      "Variable types: 0 continuous, 2 integer (0 binary)\n",
-      "Coefficient statistics:\n",
-      "  Matrix range     [1e+00, 3e+00]\n",
-      "  Objective range  [1e+00, 1e+00]\n",
-      "  Bounds range     [0e+00, 0e+00]\n",
-      "  RHS range        [2e+01, 2e+01]\n",
-      "Found heuristic solution: objective 7.0000000\n",
-      "Presolve removed 3 rows and 2 columns\n",
-      "Presolve time: 0.01s\n",
-      "Presolve: All rows and columns removed\n",
-      "\n",
-      "Explored 0 nodes (0 simplex iterations) in 0.01 seconds (0.00 work units)\n",
-      "Thread count was 1 (of 8 available processors)\n",
-      "\n",
-      "Solution count 2: 13 7 \n",
-      "\n",
-      "Optimal solution found (tolerance 1.00e-04)\n",
-      "Best objective 1.300000000000e+01, best bound 1.300000000000e+01, gap 0.0000%\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Optimize model\n",
-    "model_1.optimize()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From here we can get all the variables in the model and print their values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x -0\n",
-      "y 13\n",
-      "Obj: 13\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Show Solution\n",
-    "for v in model_1.getVars():\n",
-    "    print('%s %g' % (v.VarName, v.X))\n",
-    "print('Obj: %g' % model_1.ObjVal)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
-      "\n",
-      "CPU model: Apple M1\n",
-      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
-      "\n",
-      "Optimize a model with 5 rows, 2 columns and 6 nonzeros\n",
-      "Model fingerprint: 0xf66347ca\n",
-      "Variable types: 0 continuous, 2 integer (0 binary)\n",
-      "Coefficient statistics:\n",
-      "  Matrix range     [1e+00, 3e+00]\n",
-      "  Objective range  [1e+00, 2e+00]\n",
-      "  Bounds range     [0e+00, 0e+00]\n",
-      "  RHS range        [5e+00, 2e+01]\n",
-      "Found heuristic solution: objective 13.0000000\n",
-      "Presolve removed 5 rows and 2 columns\n",
-      "Presolve time: 0.00s\n",
-      "Presolve: All rows and columns removed\n",
-      "\n",
-      "Explored 0 nodes (0 simplex iterations) in 0.01 seconds (0.00 work units)\n",
-      "Thread count was 1 (of 8 available processors)\n",
-      "\n",
-      "Solution count 1: 13 \n",
-      "\n",
-      "Optimal solution found (tolerance 1.00e-04)\n",
-      "Best objective 1.300000000000e+01, best bound 1.300000000000e+01, gap 0.0000%\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_2=gp.Model()\n",
-    "# Create variables\n",
-    "x = model_2.addVar(vtype=GRB.INTEGER, name=\"x\")\n",
-    "y = model_2.addVar(vtype=GRB.INTEGER, name=\"y\")\n",
-    "\n",
-    "#add constraints\n",
-    "model_2.addConstr(x >= 0, \"hotdog_lower_bound\")\n",
-    "model_2.addConstr(y >= 0, \"soda_lower_bound\")\n",
-    "model_2.addConstr(x <= 5, \"hotdog_upper_bound\")\n",
-    "model_2.addConstr(y <= 5, \"soda_upper_bound\")\n",
-    "model_2.addConstr(3*x + 1.5*y <= 20, \"c1\")\n",
-    "\n",
-    "model_2.setObjective(2*x + y, GRB.MAXIMIZE)\n",
-    "\n",
-    "model_2.optimize()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x 4\n",
-      "y 5\n",
-      "Obj: 13\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Show Solution\n",
-    "for v in model_2.getVars():\n",
-    "    print('%s %g' % (v.VarName, v.X))\n",
-    "print('Obj: %g' % model_2.ObjVal)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Now you Try!**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we've introduced the basics, let's try some more problems. They look slightly different from our first example, but hopefully, they'll help you to identify the objective function and constraints. There are some practice questions to help you along as you go, so don't be afraid to make guesses and experiment!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- **Note:** from here on out in the notebook we will note explicitly say our decision variables must be positive (ex. $x>0$) as Gurobi sets this value by default when we declare variables. This can be modified, but in most applications the decision variable is positive."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Problem 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Problem Statement:\n",
-    "Problem Statement: Imagine you are managing disaster relief efforts, and you need to allocate resources between two critical supplies: water bottles ($w$) and emergency food packs ($f$). Your goal is to maximize the number of relief items delivered to an affected area.\n",
-    "\n",
-    "- Each water bottle provides essential hydration and is valued at 3 units of relief.\n",
-    "- Each food pack provides vital nutrition and is valued at 5 units of relief.\n",
-    "\n",
-    "However, you face the following constraints:\n",
-    "\n",
-    "1.\tYou have a total cargo space that can carry up to 100 units.\n",
-    "2.\tEach water bottle takes up 2 units of cargo space.\n",
-    "3.\tEach food pack takes up 4 units of cargo space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m 1) What is the Objective Function? \u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9665738424c14537879eaba040d2af80",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RadioButtons(options=(('w+f', 1), ('3w+5f', 2), ('2w+4f', 3)), value=1)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "34f723bae1c642d7bcd2cd69ef97469b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Button(description='Check', style=ButtonStyle())"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "adb54e190f594e50bdd7fea1905a9797",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m 2) Which equation represents the cargo space constraint? \u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "361d1896876e4890b0e5913b46a9739e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RadioButtons(options=(('2w+4f', 1), ('w+f', 2), ('3w+5f', 3)), value=1)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "15aff1f906af48d59715a66d5d3a6d9f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Button(description='Check', style=ButtonStyle())"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e1be915b192b4162ac3f921244cf44b7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m 3) Which equation represents the limit on the number of food packs? \u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "348da42a25044b5ab7b5ea17b3f9b2a4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RadioButtons(options=(('f <= 30', 1), ('w + f <= 50', 2), ('2w + 4f <= 100', 3)), value=1)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "69b2f1a9a492410e805e8e469ef4ec9d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Button(description='Check', style=ButtonStyle())"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "df8f0e21eee34e9e96def634016db6f1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "problem_1()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Great, now try to implement it! Remember you can always look back at the last section and if you get stuck.  We also have the answer key available as you scroll further down (so try not to read ahead)!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. Instantiate The Solver**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Instantiate Here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2 Add the Variables**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Add Vars Here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3 Add the Constraints**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Add Constraints Here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**4 Add the Objective Function**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Add Obj. Func. Here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**5 Solve!**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Solve Here!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Problem 1 Answer Key"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Remember, this is one possible solution, just because your code doesn't look identical, doesn't mean it's wrong!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
-      "\n",
-      "CPU model: Apple M1\n",
-      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
-      "\n",
-      "Optimize a model with 1 rows, 2 columns and 2 nonzeros\n",
-      "Model fingerprint: 0x451e4b4f\n",
-      "Variable types: 0 continuous, 2 integer (0 binary)\n",
-      "Coefficient statistics:\n",
-      "  Matrix range     [2e+00, 4e+00]\n",
-      "  Objective range  [3e+00, 5e+00]\n",
-      "  Bounds range     [0e+00, 0e+00]\n",
-      "  RHS range        [1e+02, 1e+02]\n",
-      "Found heuristic solution: objective 150.0000000\n",
-      "Presolve removed 1 rows and 2 columns\n",
-      "Presolve time: 0.00s\n",
-      "Presolve: All rows and columns removed\n",
-      "\n",
-      "Explored 0 nodes (0 simplex iterations) in 0.01 seconds (0.00 work units)\n",
-      "Thread count was 1 (of 8 available processors)\n",
-      "\n",
-      "Solution count 1: 150 \n",
-      "\n",
-      "Optimal solution found (tolerance 1.00e-04)\n",
-      "Best objective 1.500000000000e+02, best bound 1.500000000000e+02, gap 0.0000%\n",
-      "Optimal number of Water Bottles (w): 50\n",
-      "Optimal number of Food Packs (f): 0\n",
-      "Maximum Relief Value: 150.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#1 Instantiate The Solver\n",
-    "model = gp.Model('DisasterReliefAllocation')\n",
-    "\n",
-    "#2 Add the variables for the number of water bottles (w) and food packs (f)\n",
-    "w = model.addVar(vtype=GRB.INTEGER, name='WaterBottles')\n",
-    "f = model.addVar(vtype=GRB.INTEGER, name='FoodPacks')\n",
-    "\n",
-    "#3 Add the constraint for the total cargo space\n",
-    "model.addConstr(2 * w + 4 * f <= 100, 'CargoSpace')\n",
-    "\n",
-    "#4 Add the objective function: Maximize the total value of relief items\n",
-    "model.setObjective(3 * w + 5 * f, GRB.MAXIMIZE)\n",
-    "\n",
-    "#5 Optimize the model\n",
-    "model.optimize()\n",
-    "\n",
-    "# Print the optimal solution\n",
-    "if model.status == GRB.OPTIMAL:\n",
-    "    print(f\"Optimal number of Water Bottles (w): {int(w.x)}\")\n",
-    "    print(f\"Optimal number of Food Packs (f): {int(f.x)}\")\n",
-    "    print(f\"Maximum Relief Value: {model.objVal}\")\n",
-    "else:\n",
-    "    print(\"No optimal solution found.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Problem 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before diving into the next example, we wanted to take a second to mention a great resource by an expert on the Gurobi team talking about implementing models. It's a little more comprehensive than our current examples, but if you want to see how an expert thinks about this problem, [it's incredibly useful!](https://www.gurobi.com/resources/optimization-modeling-the-art-of-not-making-it-an-art/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- **Note:** NOTE: The following example is based on examples from [Gurobi’s MOOC for Udemy](https://www.gurobi.com/resources/intro-to-optimization-through-the-lens-of-data-science/)\n",
-    "\n",
-    "A non-govermental-organization (NGO) and a manufacturing firm are partnering up to produce supplies to prepare for hurricane season. The NGO predicts that there will be a maximum demand of up to 100 packs of blankets, 70 packs of towels, and 40 packs of sleeping bags. Let's say making these items requires two precision machining steps: weaving and packaging. The table below shows how many minutes are to produce each type of disaster relief item:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "|          | Weaving    | Packaging | \n",
-    "| -------- | ------- | -------|\n",
-    "| Blankets  | 10    |15   |\n",
-    "| Towels | 8    |18  |\n",
-    "| Sleeping Bags    | 12    |17   |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To allow for maintenance and downtime, the company does not want to run its machines beyond a certain limit. The total time available on the machines is 2,500 hours for weaving and 2,000 hours for packaging."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once items are manufactured, they go to a quality control tester. They are contracted to test exactly 150 items for this upcoming season, no more and no less. Therefore, the company must manufacture exactly 150 items."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now for the final step, let's consider how many people each pack of items can satisfy:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "|          | People Served     | \n",
-    "| -------- | ------- |\n",
-    "| Blankets  | 6   |\n",
-    "| Towels | 7   |\n",
-    "| Sleeping Bags    | 10  |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Implement your solution here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Problem 2 Answer Key"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
-      "\n",
-      "CPU model: Apple M1\n",
-      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
-      "\n",
-      "Optimize a model with 6 rows, 3 columns and 12 nonzeros\n",
-      "Model fingerprint: 0x00f9ccc8\n",
-      "Variable types: 0 continuous, 3 integer (0 binary)\n",
-      "Coefficient statistics:\n",
-      "  Matrix range     [1e+00, 2e+01]\n",
-      "  Objective range  [6e+00, 1e+01]\n",
-      "  Bounds range     [0e+00, 0e+00]\n",
-      "  RHS range        [4e+01, 2e+03]\n",
-      "Presolve removed 6 rows and 3 columns\n",
-      "Presolve time: 0.00s\n",
-      "Presolve: All rows and columns removed\n",
-      "\n",
-      "Explored 0 nodes (0 simplex iterations) in 0.01 seconds (0.00 work units)\n",
-      "Thread count was 1 (of 8 available processors)\n",
-      "\n",
-      "Solution count 1: 1083 \n",
-      "\n",
-      "Optimal solution found (tolerance 1.00e-04)\n",
-      "Best objective 1.083000000000e+03, best bound 1.083000000000e+03, gap 0.0000%\n"
-     ]
-    }
-   ],
-   "source": [
-    "m = gp.Model(\"instrument\") \n",
-    " \n",
-    "# make decision Var\n",
-    "# this is the qunatity of each instrument\n",
-    "x = m.addVars(3, vtype=GRB.INTEGER,name=\"x\") \n",
-    "m.setObjective(6*x[0] + 7*x[1] + 10*x[2],GRB.MAXIMIZE) \n",
-    "\n",
-    "m.addConstr(10*x[0] + 8*x[1]  + 12*x[2] <= 2000, name='Weaving') \n",
-    "m.addConstr(15*x[0] + 18*x[1] + 17*x[2] <= 2400, name='Packaging') \n",
-    "m.addConstr(x.sum() == 150, name='Total_Supplies') \n",
-    " \n",
-    "m.addConstr(x[0] <= 100, name='max_100_Blankets_demand') \n",
-    "m.addConstr(x[1] <= 70, name='max_70_Towels_demand') \n",
-    "m.addConstr(x[2] <= 40, name='max_40_SleepBags_demand') \n",
-    "m.optimize()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x[0] 87\n",
-      "x[1] 23\n",
-      "x[2] 40\n",
-      "Obj: 1083\n",
-      "150.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Show Solution\n",
-    "a=0\n",
-    "for v in m.getVars():\n",
-    "    print('%s %g' % (v.VarName, v.X))\n",
-    "    a+=v.X\n",
-    "print('Obj: %g' % m.ObjVal)\n",
-    "print(a)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[<gurobi.Constr Total_Supplies>, <gurobi.Constr max_40_SleepBags_demand>]\n"
-     ]
-    }
-   ],
-   "source": [
-    "binding = [c for c in m.getConstrs() if abs(c.Slack) < 1e-6]\n",
-    "print(binding)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "466.0\n",
-      "1.0\n",
-      "0.0\n",
-      "13.0\n",
-      "47.0\n",
-      "0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "for c in m.getConstrs():\n",
-    "    print(c.Slack)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.2 Intro to the ESUPS Optimization Model\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that you're familiar with the basics, let's start looking at the actual model. We'll start with a simplified version of the model, and we'll build from there.\n",
-    "\n",
-    "Consider the scenario where we're expecting to have a disaster and want to pre-position supplies in order to respond as quickly as possible. Perhaps it's hurricane season or a volcano has been showing increasing activity. As we have seen in past problems, we have to set up the variables, constraints, and objective functions. So first let's define our decision variables, i.e. the things we can vary."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let:\n",
-    "\n",
-    "- $\\tau_{ij}$ be the time to ship a single item from warehouse $i$ to the disaster\n",
-    "\n",
-    "- $y_i$ be the amount of supplies to send from warehouse $i$ to our disaster\n",
-    "\n",
-    "- $x_i$ be the starting inventory at each warehouse\n",
-    "\n",
-    "- $d$ be the demand for an item\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have described what we can change with our variables, we can figure out how to represent the objective function!\n",
-    "$$\n",
-    "\\min_y \\sum_i \\tau_{i}\\cdot y_i\n",
-    "\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and the constraints are:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\n",
-    "\n",
-    "\\text{s.t.}  & \\sum_{i} & y_{i}&=d & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n",
-    "\n",
-    "& & y_i       &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
-    "\n",
-    " &\\text{} & y_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So now let's solve this model. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Make The model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#prep distance matrix\n",
-    "df_distance, relevant_warehouses, BucketsNeeded = get_distance_matrix(dataset)\n",
-    "t = df_distance.drivingTime_hrs\n",
-    "n = len(relevant_warehouses)\n",
-    "\n",
-    "# Create model\n",
-    "model = gp.Model(\"simple_Allocation\") \n",
-    "\n",
-    "#Add Decision Var\n",
-    "y=model.addVars(n, vtype=GRB.INTEGER, name=\"Warehouse_Allocation\")\n",
-    "\n",
-    "#Add constraint to meet demand\n",
-    "model.addConstr(gp.quicksum(y[i] for i in range(n))==BucketsNeeded,name='Meet_Demand')\n",
-    "\n",
-    "# Add in warehouse_constraints\n",
-    "for i, supplies in enumerate(relevant_warehouses):\n",
-    "    model.addConstr(y[i] <= supplies[2], name=f\"warehouse_endowment_{i}\")\n",
-    "\n",
-    "# Add objective\n",
-    "objective = gp.quicksum(t[i] * y[i] for i in range(n))\n",
-    "model.setObjective(objective, GRB.MINIMIZE)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
-      "\n",
-      "CPU model: Apple M1\n",
-      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
-      "\n",
-      "Optimize a model with 33 rows, 32 columns and 48 nonzeros\n",
-      "Model fingerprint: 0xfec0ae5a\n",
-      "Model has 16 quadratic objective terms\n",
-      "Variable types: 0 continuous, 32 integer (0 binary)\n",
-      "Coefficient statistics:\n",
-      "  Matrix range     [1e+00, 1e+00]\n",
-      "  Objective range  [0e+00, 0e+00]\n",
-      "  QObjective range [2e+00, 2e+00]\n",
-      "  Bounds range     [0e+00, 0e+00]\n",
-      "  RHS range        [3e+00, 1e+04]\n",
-      "Presolved: 1 rows, 13 columns, 13 nonzeros\n",
-      "\n",
-      "Continuing optimization...\n",
-      "\n",
-      "\n",
-      "Explored 1 nodes (1 simplex iterations) in 0.01 seconds (0.00 work units)\n",
-      "Thread count was 8 (of 8 available processors)\n",
-      "\n",
-      "Solution count 3: 98293 185480 185541 \n",
-      "\n",
-      "Optimal solution found (tolerance 1.00e-04)\n",
-      "Best objective 9.829300000000e+04, best bound 9.829300000000e+04, gap 0.0000%\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.optimize()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now Let's Anaylize the results!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Var</th>\n",
-       "      <th>amount</th>\n",
-       "      <th>possible</th>\n",
-       "      <th>distance</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Warehouse_Allocation[1]</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>26</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Warehouse_Allocation[4]</td>\n",
-       "      <td>9046.0</td>\n",
-       "      <td>9046</td>\n",
-       "      <td>6.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Warehouse_Allocation[11]</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Warehouse_Allocation[14]</td>\n",
-       "      <td>1580.0</td>\n",
-       "      <td>1580</td>\n",
-       "      <td>8.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Warehouse_Allocation[5]</td>\n",
-       "      <td>610.0</td>\n",
-       "      <td>610</td>\n",
-       "      <td>10.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Warehouse_Allocation[2]</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>41</td>\n",
-       "      <td>11.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Warehouse_Allocation[7]</td>\n",
-       "      <td>2296.0</td>\n",
-       "      <td>6689</td>\n",
-       "      <td>11.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Warehouse_Allocation[0]</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>375</td>\n",
-       "      <td>14.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Warehouse_Allocation[9]</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>2460</td>\n",
-       "      <td>14.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Warehouse_Allocation[8]</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>150</td>\n",
-       "      <td>16.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Warehouse_Allocation[12]</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>736</td>\n",
-       "      <td>16.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Warehouse_Allocation[10]</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>4235</td>\n",
-       "      <td>17.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Warehouse_Allocation[6]</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>5201</td>\n",
-       "      <td>19.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Warehouse_Allocation[15]</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>1637</td>\n",
-       "      <td>22.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Warehouse_Allocation[13]</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>5700</td>\n",
-       "      <td>23.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Warehouse_Allocation[3]</td>\n",
-       "      <td>-0.0</td>\n",
-       "      <td>2322</td>\n",
-       "      <td>26.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         Var  amount  possible  distance\n",
-       "1    Warehouse_Allocation[1]    26.0        26      0.00\n",
-       "4    Warehouse_Allocation[4]  9046.0      9046      6.00\n",
-       "11  Warehouse_Allocation[11]     3.0         3      7.00\n",
-       "14  Warehouse_Allocation[14]  1580.0      1580      8.00\n",
-       "5    Warehouse_Allocation[5]   610.0       610     10.00\n",
-       "2    Warehouse_Allocation[2]    -0.0        41     11.00\n",
-       "7    Warehouse_Allocation[7]  2296.0      6689     11.00\n",
-       "0    Warehouse_Allocation[0]     0.0       375     14.00\n",
-       "9    Warehouse_Allocation[9]    -0.0      2460     14.25\n",
-       "8    Warehouse_Allocation[8]     0.0       150     16.00\n",
-       "12  Warehouse_Allocation[12]    -0.0       736     16.00\n",
-       "10  Warehouse_Allocation[10]    -0.0      4235     17.00\n",
-       "6    Warehouse_Allocation[6]    -0.0      5201     19.00\n",
-       "15  Warehouse_Allocation[15]    -0.0      1637     22.00\n",
-       "13  Warehouse_Allocation[13]    -0.0      5700     23.00\n",
-       "3    Warehouse_Allocation[3]    -0.0      2322     26.00"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Show Solution\n",
-    "b=[]\n",
-    "for v, total, dis in zip(model.getVars(),relevant_warehouses,list(df_distance['drivingTime_hrs'])):\n",
-    "    if v.VarName[0:20]== 'Warehouse_Allocation':\n",
-    "        #print('%s %g | total= %g | distance=%g' % (v.VarName, v.X,total[2],dis))\n",
-    "        b.append([v.VarName, v.X,total[2],dis])\n",
-    "b=pd.DataFrame(b)\n",
-    "b.columns=['Var','amount','possible','distance' ]\n",
-    "b.sort_values('distance')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now you may have noticed that this feels like overkill. If we want to position supplies to respond to a known disaster, you might think that we should just put them as close as possible. It's an intuitive solution that can be solved with a simple greedy algorithm. But of course, life is never that simple. \n",
-    "\n",
-    "Now that we've got the initial problem outlined, let's start making it more realistic with two additions:\n",
-    "1.\tInstead of preparing for only one disaster, let's prepare for all the disasters that might occur.\n",
-    "2.\tInstead of being an omniscient observer, let's say we aren't sure where the next disaster will be\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, these assumptions might be intimidating at first. How do you rigorously model information we don't know? Isn't this the realm of predictive models?\n",
-    "\n",
-    "This reaction is totally normal and indeed we often use predictive modeling when our information is incomplete, however, remember that this problem isn't easily suited to ML techniques due to the low availability of data, small feature space, and large solution space. So how do we approach this with optimization? Well now that we know how supplies will be dispensed for a given warehouse allocation, we can leverage the small set of historical disasters we do have to ask which allocations would have saved/minimized the time taken to respond to all of the disasters. Then our optimization techniques will give us a small number that could feasibly satisfy this (where our constraints meet, see the introduction for more on this) and we can just iterate over them.\n",
-    "\n",
-    "So how do we go about it in practice?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.3 Including All Disasters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we begin, let's explicitly define our new problem with the additional requirements outlined in the previous section so we're all on the same page. Our first step is to add in the fact that there are more disasters than just one. We can do that by including a variable to denote which disaster we're talking about\n",
-    "\n",
-    "Let:\n",
-    "\n",
-    "- $k$ the disaster scenario at hand i.e. a storm, earthquake, or epidemic\n",
-    "\n",
-    "- $j$ be the location of the disaster"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In scenario $k$ with the disaster located at $j$, the time for a warehouse $i$  to send $y_i$ items is "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " $$\\tau_{ij}\\cdot y^k_i$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Repeating for all warehouses gives us"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$  \\sum_i \\tau_{ij}\\cdot y^k_i $$\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Repeating for all disasters gives us"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$ \\sum_k \\sum_i \\tau_{ij}\\cdot y^k_i $$\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This formulation doesn't change any of the solutions we found in the last section, instead, it's leveraging the power of our notation to be able to solve for the optimal allocation for every disaster in one fell swoop! It's been a while since we looked at the original problem in its entirety, so let's take a step back to understand why this is so important.\n",
-    "\n",
-    "The question that ESUPS, and subsequently this model, set out to solve is: where should disaster relief supplies be stored in a country to ensure it is as fast and cheap as possible to get them to people in need in the event of a disaster<sup>1</sup>? By comparing the overall travel time for every single disaster that we have data on, we can make determinations on whether storing different quantities of materials in different warehouses would be better or worse given our historical data.\n",
-    "\n",
-    "\n",
-    "<sup>1: Remember that the model we're looking at is specifically for time, to solve for cost we would replace our time variable with a cost variable  </sup> $^{\\tau_{ij}}$ <sup>with a cost variable </sup> $^{c_{ij}}$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we get to solving, let's add in one more small factor. Different disasters occur at different rates. A landlocked nation may be less likely to experience a disaster caused by a tropical storm than an earthquake, so shouldn't we weigh the response time to earthquakes more than that of a storm?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.4 Accounting for Randomness"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One of the most difficult tasks that we can encounter in data science problems is randomness or as it's often called \"stochastic\" elements. This can be seen in all kinds of ways, but in our case study, we're going to look at how we account for not knowing which disaster will hit next or even several years in the future. The way we do this is to create a stochastic model, which may initially seem intimidating, but in discrete events like this, it's super easy.\n",
-    "\n",
-    "If you're familiar with expected value this will quickly make sense, but no need to have any prior experience! The idea is that we weigh the outcome of the event (i.e. total travel time in this case) by the probability it occurs. So, if an earthquake is 3 times as likely as a flood, we would rewrite the equation as:\n",
-    "\n",
-    "$$.75 \\cdot\\text{(travel time earthquake)} + .25 \\cdot\\text{(travel time flood)} $$\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.4.1 More On Expected Value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you haven't seen expected value before or if it's just a been a while and you'd like a refresher, try some of the practice problems below!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When we solve expected value for discrete outcomes, we take the value/outcome/payoff of each possible event and discount it by the probability that it actually occurs. So it follows the form\n",
-    "\n",
-    "$$\n",
-    "E[X]=p_1(x_1)+p_2(x_2)+ ... + p_n(x_n)\n",
-    "$$\n",
-    "To simplify this notation we typically write this as a series:\n",
-    "\n",
-    "$$\n",
-    "E[X]=\\sum_{i=1}^{i=n} p_i \\cdot x_i\n",
-    "$$\n",
-    "\n",
-    "Try some of the problems below to get a better feel for it!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Starter Problems**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Coin Flip: "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose we play a coin flipping game. Every time we flip the coin and it lands on heads, you get a dollar, and every time it lands on tails, you have to pay a dollar. How much are you expected to make or lose when playing this game?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m 1) What is the Objective Function? \u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5fdae22271cf48789dc0fd35180320c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RadioButtons(options=(('-1', 1), ('0', 2), ('1', 3)), value=1)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fa0898d5db0a4eb181b6acb3c3a78825",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Button(description='Check', style=ButtonStyle())"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "334b1d4f419344a28bb421d64457b720",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "q_3_1()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Coin Flip Varient: "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's change the game a little, say you now get 5 paid dollars when the coin lands on heads and only have to pay 3 dollars when it lands on tails. Would you play? How much are you expected to make or lose when playing this game?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m 1) What is the Objective Function? \u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4fad507aa29242c189e56dfa436d6599",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RadioButtons(options=(('0', 1), ('1/2', 2), ('1', 3)), value=1)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3e40781cb87e476f8264dce0878a42eb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Button(description='Check', style=ButtonStyle())"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6f10f550a65b4565b18ea8c58f07d3c7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "q_3_2()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Simple Dice Game:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Suppose you roll a fair six-sided die. If it lands on 6, you win $10; otherwise, you lose $2. What is the expected value of this game? Should you play it?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Solution"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "E[X] &= \\left(\\frac{1}{6} \\times 10\\right) + \\left(\\frac{5}{6} \\times (-2)\\right)\n",
-    "\\\\\n",
-    "E[X] &= \\left(\\frac{10}{6}\\right) + \\left(\\frac{-10}{6}\\right)\n",
-    "\\\\\n",
-    "E[X] &= \\frac{10 - 10}{6} = 0\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "**Answer**: The expected value is $\\$0$. This means, on average, you neither gain nor lose money from playing this game.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Two-Coin Game:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "You flip two fair coins. If both coins land on heads, you win $8. If one coin lands on heads and the other on tails, you win $4. If both coins land on tails, you lose $5. What is the expected value of playing this game?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Solution:\n",
-    "\n",
-    "\n",
-    "- Probability of two heads (HH): $\\frac{1}{4}$\n",
-    "- Probability of one head and one tail (HT or TH): $\\frac{1}{2}$\n",
-    "- Probability of two tails (TT): $\\frac{1}{4}$\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "E[X] &= \\left(\\frac{1}{4} \\times 8\\right) + \\left(\\frac{1}{2} \\times 4\\right) + \\left(\\frac{1}{4} \\times (-5)\\right)\n",
-    "\\\\\n",
-    "E[X] &= \\left(2\\right) + \\left(2\\right) + \\left(-1.25\\right)\n",
-    "\\\\\n",
-    "E[X] &= 2 + 2 - 1.25 = 2.75\n",
-    "\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "**Answer**: The expected value is $\\$2.75$. This means, on average, you gain $\\$2.75$ per game.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Intermediate Problems"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Custom Die Game:**\n",
-    "Imagine a custom die with faces labeled 1, 2, 3, 4, 5, and 6. Each face has a different probability: \\(P(1) = 0.1\\), \\(P(2) = 0.2\\), \\(P(3) = 0.15\\), \\(P(4) = 0.25\\), \\(P(5) = 0.2\\), and \\(P(6) = 0.1\\). If the outcome of the die is 1, you win $2; for a 2, you win $4; for a 3, you win $6; for a 4, you lose $3; for a 5, you lose $7; and for a 6, you lose $5. What is the expected value of this game?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Solution:\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "E[X] &= (0.1 \\times 2) + (0.2 \\times 4) + (0.15 \\times 6) + (0.25 \\times -3) + (0.2 \\times -7) + (0.1 \\times -5)\n",
-    "\n",
-    "\\\\\n",
-    "E[X] &= (0.2) + (0.8) + (0.9) + (-0.75) + (-1.4) + (-0.5)\n",
-    "\\\\\n",
-    "\n",
-    "E[X] &= 1.9 - 2.65 = -0.75\n",
-    "\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "**Answer**:The expected value is $-\\$0.75$. On average, you lose $75$ cents per roll.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Card Drawing Game:**\n",
-    "You draw a card from a standard deck of 52 playing cards. If you draw an Ace, you win $15. If you draw a King, Queen, or Jack, you win $5. For any other card, you lose $3. What is the expected value of this game?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Solution:\n",
-    "\n",
-    "- Probability of drawing an Ace: $\\frac{4}{52} = \\frac{1}{13}$\n",
-    "- Probability of drawing a King, Queen, or Jack: $\\frac{12}{52} = \\frac{3}{13}$\n",
-    "- Probability of drawing any other card: $\\frac{36}{52} = \\frac{9}{13}$\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\n",
-    "E[X] &= \\left(\\frac{1}{13} \\times 15\\right) + \\left(\\frac{3}{13} \\times 5\\right) + \\left(\\frac{9}{13} \\times (-3)\\right)\n",
-    "\\\\\n",
-    "\n",
-    "\n",
-    "E[X] &= \\left(\\frac{15}{13}\\right) + \\left(\\frac{15}{13}\\right) + \\left(\\frac{-27}{13}\\right)\n",
-    "\\\\\n",
-    "\n",
-    "\n",
-    "E[X] &= \\frac{15 + 15 - 27}{13} = \\frac{3}{13}\n",
-    "\n",
-    "\\\\\n",
-    "E[X] &\\approx 0.23\n",
-    "\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "**Answer**: The expected value is approximately \\$0.23. On average, you gain 23 cents per draw.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "##### Advanced Extension Problems \n",
-    "\n",
-    "5. **Roulette Game:**\n",
-    "   You bet $1 on a single number in a game of American Roulette. If the ball lands on your number, you win $35; otherwise, you lose your bet. There are 38 slots on an American Roulette wheel (numbers 1-36, 0, and 00). What is the expected value of this bet?\n",
-    "\n",
-    "6. **Insurance Policy:**\n",
-    "   An insurance company sells a one-year term life insurance policy for $500 to a healthy 30-year-old. The policy pays $100,000 in case of death within the year. The probability of a healthy 30-year-old dying within a year is 0.001. What is the expected profit for the insurance company from selling this policy?\n",
-    "\n",
-    "7. **Stock Investment Scenario:**\n",
-    "   Suppose you invest in a stock with three possible outcomes over a year: there is a 50% chance it will increase by 10%, a 30% chance it will decrease by 5%, and a 20% chance it will decrease by 15%. What is the expected value of the return on investment after one year?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### The Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we've had some background on expected value, let's return to our model! Remeber out goal is to account for the inherent randomness associated with diffrent disaster occuring  \n",
-    "\n",
-    "Let $P^k$ be the probability of disaster $k$ and $t^k$ be the total travel time involved in disaster $k$ in the previous sections. Using our definition of expected value, this gives us\n",
-    "\n",
-    "$$ \\sum_k P^k \\cdot t^k$$\n",
-    "\n",
-    "Now you might notice that we have already written an equation for the total travel time for disaster $k$ ! Substituting this in we get\n",
-    "\n",
-    "$$\\sum_k P^k \\sum_i \\tau_{ij}\\cdot y^k_i$$\n",
-    "\n",
-    "So, you can see, in our case turning this problem into a stochastic optimization problem that can account for probability only involves one more variable. So, our final task right now is to minimize the time required to get supplies to a disaster given how likely each disaster is to occur:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "$$\n",
-    "\\min_y \\sum_k P^k \\sum_i \\tau_{ij}\\cdot y^k_i\n",
-    "\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can see the constraints are almost unchanged. The only added part is that all probabilities must sum to one, which is always the case:\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\n",
-    "\n",
-    "\\text{s.t.}  & \\sum_{i} & y^k_{i}&=d^k & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n",
-    "\n",
-    "& & y^k_i       &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
-    "\n",
-    " &\\text{} & y^k_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n",
-    "\n",
-    "& \\sum_k & P^k &=1 && \\hspace{.2cm} \\text{(All probabilities must sum to 1)}\\\\\n",
-    "\n",
-    "\n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "How do we calculate the probability though? Well, we have good long-term data on what disasters have affected which countries. For now, we can go through that data and calculate the probability of disaster k by counting how many times it has occurred and dividing by the number of total disasters i.e.\n",
-    "\n",
-    "$$ P^k = \\frac{k}{\\|K\\|}$$\n",
-    "\n",
-    "You may have noticed that every single disaster will have the same probability, or in other words, this will produce a uniform distribution. As a result, $P^k$ is a constant and can effectively be removed, so our new equation will give the exact same results as our old one. We'll talk about ways to potentially address this later on in the notebooks as an extension, but for the moment, the purpose of adding this is to show how easily the equation can be modified to be stochastic and to get users used to the idea."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So now that we have the equation, we can let the model go ahead and let Gurobi solve it for us"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will start using ```with``` statements to accomidate the model's growing size and need for our License"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[14, 0, 11, 26, 6, 10, 19, 11, 16, 14, 17, 7, 16, 23, 8, 22]\n",
-      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
-      "\n",
-      "CPU model: Apple M1\n",
-      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
-      "\n",
-      "Optimize a model with 1088 rows, 1024 columns and 2048 nonzeros\n",
-      "Model fingerprint: 0xad7c1256\n",
-      "Variable types: 0 continuous, 1024 integer (0 binary)\n",
-      "Coefficient statistics:\n",
-      "  Matrix range     [1e+00, 1e+00]\n",
-      "  Objective range  [9e-02, 4e-01]\n",
-      "  Bounds range     [0e+00, 0e+00]\n",
-      "  RHS range        [3e+00, 4e+04]\n",
-      "Presolve removed 1087 rows and 1011 columns\n",
-      "Presolve time: 0.01s\n",
-      "Presolved: 1 rows, 13 columns, 13 nonzeros\n",
-      "Variable types: 0 continuous, 13 integer (0 binary)\n",
-      "Found heuristic solution: objective 330077.56250\n",
-      "Found heuristic solution: objective 329939.28125\n",
-      "\n",
-      "Root relaxation: objective 3.270432e+05, 1 iterations, 0.00 seconds (0.00 work units)\n",
-      "\n",
-      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
-      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
-      "\n",
-      "*    0     0               0    327043.18750 327043.188  0.00%     -    0s\n",
-      "\n",
-      "Explored 1 nodes (1 simplex iterations) in 0.02 seconds (0.00 work units)\n",
-      "Thread count was 8 (of 8 available processors)\n",
-      "\n",
-      "Solution count 3: 327043 329939 330078 \n",
-      "\n",
-      "Optimal solution found (tolerance 1.00e-04)\n",
-      "Best objective 3.270431875000e+05, best bound 3.270431875000e+05, gap 0.0000%\n"
-     ]
-    }
-   ],
-   "source": [
-    "demand, probs = get_probs(dataset)\n",
-    "\n",
-    "n = len(relevant_warehouses)\n",
-    "m = len(demand)\n",
-    "a = []\n",
-    "\n",
-    "# Create an array of driving times based on the df_distance DataFrame\n",
-    "t = [int(row['drivingTime_hrs']) for i, row in df_distance.iterrows()]\n",
-    "c=pd.DataFrame(t)\n",
-    "print(t)\n",
-    "\n",
-    "# Amount to take per Warehouse\n",
-    "y = model.addVars(m, n, vtype=GRB.INTEGER, name=\"Warehouse_Allocation\")\n",
-    "\n",
-    "# Add constraints to meet demand for each disaster scenario (k)\n",
-    "for k in range(m):\n",
-    "    # Demand constraints\n",
-    "    model.addConstr(gp.quicksum(y[k, i] for i in range(n)) == demand[k], name=f\"Meet_Demand_K:{k}\")\n",
-    "    \n",
-    "    # Warehouse constraints\n",
-    "    for i, supplies in enumerate(relevant_warehouses):\n",
-    "        model.addConstr(y[k, i] <= supplies[2], name=f\"warehouse_endowment_K:{k}_I:{i}\")\n",
-    "\n",
-    "# Objective function to minimize the weighted driving time using T as a parameter\n",
-    "objective = gp.quicksum(\n",
-    "    probs[k] * gp.quicksum(t[i] * y[k, i] for i in range(n))\n",
-    "    for k in range(m)\n",
-    ")\n",
-    "\n",
-    "# Optimize model\n",
-    "model.setObjective(objective, GRB.MINIMIZE)\n",
-    "model.optimize()\n",
-    "\n",
-    "# Store results in the list 'a'\n",
-    "for v in model.getVars():\n",
-    "    a.append([v.VarName, v.X])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Warehouse_Allocation[0,0]</td>\n",
-       "      <td>375.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Warehouse_Allocation[0,1]</td>\n",
-       "      <td>26.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Warehouse_Allocation[0,2]</td>\n",
-       "      <td>41.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Warehouse_Allocation[0,3]</td>\n",
-       "      <td>2322.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Warehouse_Allocation[0,4]</td>\n",
-       "      <td>9046.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Warehouse_Allocation[0,5]</td>\n",
-       "      <td>610.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Warehouse_Allocation[0,6]</td>\n",
-       "      <td>5201.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Warehouse_Allocation[0,7]</td>\n",
-       "      <td>6689.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Warehouse_Allocation[0,8]</td>\n",
-       "      <td>150.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Warehouse_Allocation[0,9]</td>\n",
-       "      <td>2460.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Warehouse_Allocation[0,10]</td>\n",
-       "      <td>4235.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Warehouse_Allocation[0,11]</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Warehouse_Allocation[0,12]</td>\n",
-       "      <td>736.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Warehouse_Allocation[0,13]</td>\n",
-       "      <td>5700.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Warehouse_Allocation[0,14]</td>\n",
-       "      <td>1580.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                             0       1\n",
-       "0    Warehouse_Allocation[0,0]   375.0\n",
-       "1    Warehouse_Allocation[0,1]    26.0\n",
-       "2    Warehouse_Allocation[0,2]    41.0\n",
-       "3    Warehouse_Allocation[0,3]  2322.0\n",
-       "4    Warehouse_Allocation[0,4]  9046.0\n",
-       "5    Warehouse_Allocation[0,5]   610.0\n",
-       "6    Warehouse_Allocation[0,6]  5201.0\n",
-       "7    Warehouse_Allocation[0,7]  6689.0\n",
-       "8    Warehouse_Allocation[0,8]   150.0\n",
-       "9    Warehouse_Allocation[0,9]  2460.0\n",
-       "10  Warehouse_Allocation[0,10]  4235.0\n",
-       "11  Warehouse_Allocation[0,11]     3.0\n",
-       "12  Warehouse_Allocation[0,12]   736.0\n",
-       "13  Warehouse_Allocation[0,13]  5700.0\n",
-       "14  Warehouse_Allocation[0,14]  1580.0"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(a).iloc[:15]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "40811"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.5 Data Science Extension"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We've seen how the equation uses the uniform distribution to solve the problem, but what if we knew something it didn't? What if knowing that climate change is increasingly energizing large storms, we decide the past hurricane impacts aren't representative of what's to come? In this section we want to prompt you to come up with predictive elements to improve our models. Feel free to use some of the ideas below or go in an entirely new direction!\n",
-    "\n",
-    "In this section, we encourage you to think creatively about enhancing predictive models for climate-related disasters. Consider how to incorporate novel data sources, feature engineering techniques, and model architectures to improve predictions. Below are some suggested approaches, but feel free to explore entirely new directions!\n",
-    "\n",
-    "Case Study Focus: Coastal Eastern African Nations\n",
-    "\n",
-    "Using the disaster impact data for coastal Eastern African nations, can you develop a model to predict how these impacts might escalate for Madagascar in the coming years? Consider not only the historical data but also factors such as changes in sea surface temperatures, shifting storm tracks, population growth along vulnerable coastlines, and evolving infrastructure resilience. Further, can you integrate this predictive model into an optimization framework to better allocate resources for disaster preparedness and response?\n",
-    "\n",
-    "Potential Approaches to Explore:\n",
-    "1. Comparing Time Series Models: Traditional statistical time series models like ARIMAX (AutoRegressive Integrated Moving Average with Explanatory Variables) are commonly used to predict future values based on past data. How do these models compare with more advanced Recurrent Neural Network (RNN)-based approaches like Long Short-Term Memory (LSTM) networks or Gated Recurrent Units (GRUs) in capturing long-term dependencies, especially under non-stationary conditions induced by climate change?\n",
-    "\n",
-    "2.\tIncorporating Geospatial Data: Geospatial features, such as latitude, longitude, elevation, and proximity to bodies of water, can play a crucial role in predicting the impact of tropical storms. Can we encode geospatial information using techniques like convolutional neural networks (CNNs) for spatial feature extraction, or leverage more specialized models such as Geographical Weighted Regression (GWR) or Graph Neural Networks (GNNs) to account for spatial dependencies?\n",
-    "\n",
-    "3.\tIncorporating Climate Change Projections: Beyond just historical data, consider how future climate projections can be integrated into the model. Can we use downscaled climate model outputs or ensemble approaches to account for different climate scenarios? How would these scenarios affect the frequency and intensity of tropical storms affecting coastal Eastern African nations?\n",
-    "\n",
-    "4.\tFeature Engineering with Climate Indicators: Introduce climate change indicators as predictive features. For example, how do trends in sea surface temperatures (SSTs), El Niño-Southern Oscillation (ENSO) phases, or the Atlantic Multi-decadal Oscillation (AMO) correlate with storm intensification? Would incorporating these indicators as additional time series variables enhance predictive accuracy?\n",
-    "\t\n",
-    "5.\tHybrid and Ensemble Models: Can we leverage hybrid models that combine both statistical and deep learning approaches, or use ensemble methods that aggregate predictions from multiple models? For example, combining ARIMAX for short-term predictions with LSTM for capturing long-term trends may provide a more comprehensive forecasting tool.\n",
-    "\n",
-    "6.\tOptimization Integration: Once a reliable predictive model is established, how can it be integrated into an optimization framework for resource allocation? For example, can we build an optimization model that minimizes both the cost of disaster preparedness and the potential loss from future storm impacts?\n",
-    "\n",
-    "7.\tModel Explainability and Decision-Making: How can we ensure that the model is interpretable for decision-makers? Consider the use of techniques like SHAP (SHapley Additive exPlanations) values or LIME (Local Interpretable Model-agnostic Explanations) to explain which factors contribute most to the model’s predictions, helping policymakers make informed decisions.\n",
-    "\n",
-    "By combining predictive analytics with optimization, we can not only forecast future disaster impacts but also develop actionable strategies for minimizing those impacts. The goal is to make our models both more accurate and more useful in real-world applications, driving better outcomes for communities at risk.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.6 Processing the Results: 2-Stage SLP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Great, now that we can solve for the time it takes to address every single disaster, we can finally answer the original question posed: how do we best allocate supplies to all the warehouses? The key here is to create a second optimization problem. Remember how in our constraints we included that you can't send more than the warehouse has? \n",
-    "\n",
-    "$$\n",
-    "y^k_i       \\leq x_i  \\forall i \\in I \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
-    "$$\n",
-    "\n",
-    "So far, we've just been using the actual allocation we have at each warehouse for $x_i$. But what if those changes? Suddenly we would have an entirely new solution. So, if we say that the output of Gurobi (i.e. the allocations $y^k_i \\in Y$) is some function based on our starting amount warehouses ($X$ where $x_i\\in X$), then we can say\n",
-    "\n",
-    "$$Y=f(X)$$ \n",
-    "\n",
-    "And when we frame it this way, it becomes much more simple to solve! All we need to do is minimize the travel times $Y$. In other words, our problem becomes \n",
-    "\n",
-    "\n",
-    "$$ \\min_{X} f(X) $$\n",
-    "$$\\begin{aligned}\n",
-    "\\text{s.t.}  & \\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n",
-    "\n",
-    " && x_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n",
-    "\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "Where $\\chi$ is the total amount of supplies we have in the country\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "While this may initially look intimidating, it is one of the easiest changes to make to our current code. All we are doing is making a variable instead of a constraint and then constraining it. Let’s substitute back in our equation from the last section with the new constraints to see this firsthand. To denote a new decision variable (i.e. a variable that can be changed), all we need to do is add it under the minimization sign. This means minimizing with respect to $X$ and $Y$\n",
-    "\n",
-    "$$\n",
-    "\\min_{X,Y} \\sum_k P^k \\sum_i \\tau_{ij}\\cdot y^k_i\n",
-    "$$\n",
-    "\n",
-    "Then all we need to do is update the constraints. I've included the line to make it easier to see what's new as our list grows. It has no mathematical significance. \n",
-    "So how do we Implement this in Gurobi?\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\n",
-    "\n",
-    "\\text{s.t.}  & \\sum_{i} & y^k_{i}&=d^k & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n",
-    "\n",
-    "& & y^k_i       &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
-    "\n",
-    " &\\text{} & y^k_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n",
-    "\n",
-    "& \\sum_k & P^k &=1 && \\hspace{.2cm} \\text{(All probabilities must sum to 1)}\\\\\n",
-    "\n",
-    "\\hline \\\\\n",
-    "\n",
-    "  & \\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n",
-    "\n",
-    " && x_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n",
-    "\n",
-    "\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So how do we Implment this in Gurobi?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
-      "\n",
-      "CPU model: Apple M1\n",
-      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
-      "\n",
-      "Optimize a model with 1153 rows, 1040 columns and 2192 nonzeros\n",
-      "Model fingerprint: 0x0e8cfb4c\n",
-      "Variable types: 0 continuous, 1040 integer (0 binary)\n",
-      "Coefficient statistics:\n",
-      "  Matrix range     [1e+00, 1e+00]\n",
-      "  Objective range  [9e-02, 4e-01]\n",
-      "  Bounds range     [0e+00, 0e+00]\n",
-      "  RHS range        [3e+00, 4e+04]\n",
-      "Presolve removed 1152 rows and 1026 columns\n",
-      "Presolve time: 0.01s\n",
-      "Presolved: 1 rows, 14 columns, 14 nonzeros\n",
-      "Variable types: 0 continuous, 14 integer (0 binary)\n",
-      "Found heuristic solution: objective 330422.47266\n",
-      "\n",
-      "Root relaxation: objective 3.274030e+05, 1 iterations, 0.00 seconds (0.00 work units)\n",
-      "\n",
-      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
-      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
-      "\n",
-      "*    0     0               0    327403.00000 327403.000  0.00%     -    0s\n",
-      "\n",
-      "Explored 1 nodes (1 simplex iterations) in 0.03 seconds (0.00 work units)\n",
-      "Thread count was 8 (of 8 available processors)\n",
-      "\n",
-      "Solution count 2: 327403 330422 \n",
-      "\n",
-      "Optimal solution found (tolerance 1.00e-04)\n",
-      "Best objective 3.274030000000e+05, best bound 3.274030000000e+05, gap 0.0000%\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = gp.Model(\"full_allocation\")\n",
-    "\n",
-    "n = len(relevant_warehouses)\n",
-    "m = len(demand)\n",
-    "\n",
-    "# Create an array of driving times based on the df_distance DataFrame\n",
-    "t = df_distance.drivingTime_hrs\n",
-    "\n",
-    "# Amount to take per Warehouse\n",
-    "y = model.addVars(m, n, vtype=GRB.INTEGER, name=\"Single_Warehouse_Allocation\")\n",
-    "\n",
-    "# National Allocation\n",
-    "X = model.addVars(n, vtype=GRB.INTEGER, name=\"National_Allocation\")\n",
-    "\n",
-    "# Total national endowment constraint\n",
-    "model.addConstr(gp.quicksum(X[i] for i in range(n)) == 40811, name=\"Total_National_Endowment\")\n",
-    "\n",
-    "for k in range(m):\n",
-    "    model.addConstr(y[k, i] <= X[i])\n",
-    "\n",
-    "# Demand and warehouse constraints for each scenario\n",
-    "for k in range(m):\n",
-    "    model.addConstr(gp.quicksum(y[k, i] for i in range(n)) == demand[k], name=f\"Meet_Demand_K:{k}\")\n",
-    "    for i, supplies in enumerate(relevant_warehouses):\n",
-    "        model.addConstr(y[k, i] <= supplies[2], name=f\"warehouse_endowment_K:{k}_I:{i}\")\n",
-    "\n",
-    "# Objective function to minimize the weighted driving time using T as a parameter\n",
-    "objective = gp.quicksum(\n",
-    "    probs[k] * gp.quicksum(t[i] * y[k, i] for i in range(n))\n",
-    "    for k in range(m)\n",
-    ")\n",
-    "\n",
-    "# Optimize model\n",
-    "model.setObjective(objective, GRB.MINIMIZE)\n",
-    "model.optimize()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.7 A Few Final Tidbits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have our overall framework, we can extend it fairly easily to better model real-life scenarios. Let's look at two final pieces of the puzzle that ESUPS considers in their model:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Cost**\n",
-    "\n",
-    "Along with how long it takes to get items to a disaster relief site, it's also important to consider the cost to accomplish it. It might be a few hours faster to charter a jet to deliver blankets in the aftermath of a disaster, however, if it is 100x more expensive than by truck, that may constrain the organization from buying more blankets, chartering more trucks, or making it difficult to resupply for future disasters. So just as we solve for ways to minimize time, it can be important for firms with limited resources to make sure their money is being used to do the most good it can.\n",
-    "\n",
-    "So how do we do this? It's fairly simple. Our time matrix, which we've been using to show how close or far buildings are from the disaster relief site, is just a set of predefined weights/discounts. So, if we change the numbers to reflect the cost of transit, then suddenly we're solving a cost-minimization problem! In fact, the substitution is so one-to-one, that besides switching $\\tau_{ij}$ for $c_{ij}$, we don't have to change the equation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Travel Mode**\n",
-    "\n",
-    "The second additional facet considered in our real-life model that we haven't encountered yet is transportation mode. We alluded to it a little in the cost section, but often there is the option to fly or ship goods into a region, which can be especially useful when far away or the roads are clogged or otherwise unusable (such is often the case after a disaster).\n",
-    "\n",
-    "So how do we implement this? Well let's take a look back at $y_i^k$, our variable which says how many goods to send from warehouse $i$ to disaster $k$. All we want to do is reflect and updated description: how many goods to send from warehouse $i$ to disaster $k$ via mode $r$. This can easily be represented as $y_{ir}^k$, let's explain what's happened. Before is $y$ was an array of length $K$ with each index holding sub array of length $I$ (which we could also write as size $K \\times I$), now each index in our subarrays also have an array of length $3$ to represent how much is sent via truck, plane, or boat. So our final array is of dimensions $K \\times I \\times R $. This may seem intimidating at first, but remember, adding a dimension just means adding one more nested for loop!\n",
-    "\n",
-    "Let's look at how we would implement this. Remember, from a math point of view, all we've done is say $y_i^k$ can be broken down into $3$ modes instead of 1. So, it's rewritten as"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "$$\n",
-    "\\min_{X,Y} \\sum_k P^k \\sum_i \\sum_r \\tau_{irj}\\cdot y^k_{ir}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "Then all we need to do is update the constraints. I've included the line to make it easier to see what's new as our list grows. It has no mathematical significance. \n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\n",
-    "\n",
-    "\\text{s.t.}  & \\sum_{i}\\sum_{r} & y^k_{ir}&=d^k & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n",
-    "\n",
-    "& \\sum_{r}  & y^k_{ir}       &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
-    "\n",
-    " &\\text{} & y^k_{ir} &\\geq 0 &\\forall r\\in R, i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n",
-    "\n",
-    "& \\sum_k & P^k &=1 && \\hspace{.2cm} \\text{(All probabilities must sum to 1)}\\\\\n",
-    "\n",
-    "\n",
-    "  & \\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n",
-    "\n",
-    " && x_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n",
-    "\n",
-    "\n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We've added a few more sums here, but remember, in math, a sum is just a for loop. $\\sum_r$ is the equivalent to `for r in R:`. So how would we implement it in the format we've been using so far? This is going to be left as an open-ended exercise to the reader! If you get stuck, you can reference the production solver we'll be exploring below, which includes the mode of travel but is set up in a different approach than we've been using so far!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Open-Ended Implementation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Write your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.7 Interpreting the Solution "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So let's look at how much slower our real life allocations are in comparison to the optimal."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Enhancing System Performance with the Balance Metric\n",
-    "\n",
-    "In humanitarian logistics, the efficiency of inventory allocation directly impacts the ability to respond swiftly and cost-effectively to disasters. The **Balance Metric** $(D)$ is a critical tool developed to evaluate the alignment of current inventory distribution with an optimal allocation. This metric is particularly valuable in contexts where multiple organizations independently manage inventory across various depots, without a centralized coordination mechanism.\n",
-    "\n",
-    "#### Definition and Calculation of the Balance Metric\n",
-    "\n",
-    "The balance metric $(D)$ is defined as the ratio between the actual objective value (either cost or time) of the current inventory allocation $V(X)$ and the optimal objective value $V(A')$ given the same overall capacity:\n",
-    "\n",
-    "$$ D = \\frac{V(A)}{V(A')}  $$\n",
-    "\n",
-    "Here:\n",
-    "-  $V(A)$: Represents the current system-wide cost or time to meet demand based on the existing inventory allocation \\(X\\).\n",
-    "-  $V(A')$: Represents the minimized cost or time if the inventory were optimally distributed across all depots.\n",
-    "\n",
-    "#### Interpretation of the Balance Metric\n",
-    "\n",
-    "1. **Optimal Inventory Allocation**:\n",
-    "   The optimal value of $D$ is 1. This occurs when the current allocation perfectly aligns with the optimal allocation, meaning no further reallocation could reduce costs or response times.\n",
-    "\n",
-    "2. **Identifying Imbalances**:\n",
-    "   When $D > 1$, the system is considered \"out-of-balance.\" A value of 1.2, for example, implies that the current allocation incurs 20% higher costs or longer response times compared to an optimal arrangement. This indicates a potential for improvement by reallocating resources more effectively.\n",
-    "\n",
-    "3. **Guiding System Improvements**:\n",
-    "   The balance metric is not only an indicator of inefficiency but also a guide for decision-making. By identifying locations or items with the highest imbalance, decision-makers can prioritize inventory reallocations that would yield the most significant improvements in terms of cost savings or faster response times.\n",
-    "\n",
-    "#### Practical Applications in Humanitarian Logistics\n",
-    "\n",
-    "The balance metric offers several practical applications for optimizing humanitarian response efforts:\n",
-    "\n",
-    "- **Strategic Reallocation of Resources**:\n",
-    "  Organizations can use the balance metric to identify under-stocked or over-stocked depots and adjust inventory levels accordingly. This strategic reallocation can significantly enhance response times or reduce costs, especially in multi-organizational contexts where coordination is limited.\n",
-    "\n",
-    "- **Sensitivity to Network Changes**:\n",
-    "  The balance metric is responsive to changes in the logistics network. For example, if a new depot is added in a high-risk area and remains under-stocked, the balance metric will reflect this imbalance, prompting an assessment of whether inventory should be redistributed to better leverage the new depot.\n",
-    "\n",
-    "- **Decision-Making in Real-Time Operations**:\n",
-    "  By continuously monitoring the balance metric as part of a real-time dashboard, operational managers can be alerted to changes that may impact overall system performance. This enables them to make data-driven decisions quickly, improving the overall resilience and responsiveness of the humanitarian supply chain.\n",
-    "\n",
-    "#### Limitations and Considerations\n",
-    "\n",
-    "While the balance metric provides valuable insights into inventory allocation efficiency, it is important to consider its limitations:\n",
-    "\n",
-    "- **Impact of Extreme Events**:\n",
-    "  The balance metric can be influenced by extreme scenarios, such as very large-scale disasters that significantly impact the calculated demand. As a result, it should be interpreted alongside other metrics, such as the fraction of demand served ($g$) or the weighted fraction of disasters completely served ($d$), to provide a more comprehensive picture of system performance.\n",
-    "\n",
-    "- **Dependence on Data Quality and Model Assumptions**:\n",
-    "  The accuracy of the balance metric depends on the quality of input data and the assumptions made in the model. Ensuring robust and accurate data collection processes and regularly updating model parameters to reflect real-world conditions are essential for maintaining the reliability of the metric.\n",
-    "\n",
-    "#### Conclusion\n",
-    "\n",
-    "The balance metric $D$ offers a powerful tool for evaluating and improving the efficiency of inventory allocation in humanitarian logistics. By identifying imbalances and guiding strategic reallocation decisions, this metric can help organizations optimize their response efforts, ensuring that resources are used most effectively to meet the needs of affected populations during disasters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='End'></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Production Code"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When converting our code snippets into production-level code, we have a few goals,\n",
-    "1. **Readability**:\tOur code should be easy to understand and follow, even for someone who didn’t write it. Clear naming conventions, well-structured functions, and comments where necessary help ensure that others can read and maintain the code effectively.\n",
-    "\n",
-    "\n",
-    "2.\t**Robustness**: The code needs to handle different edge cases and potential errors gracefully, without breaking. This means writing code that checks for invalid inputs, unexpected conditions, and includes error handling to keep the program running smoothly.\n",
-    "\n",
-    "3.\t**Testing**: Code should be tested thoroughly to ensure it works as expected in various scenarios. This involves writing test cases that cover different functionalities, both common and rare, to catch bugs early and maintain confidence in the code’s reliability.\n",
-    "\n",
-    "To achieve these goals, we need a solid design approach that provides structure and flexibility to our code. This is where Object-Oriented Programming (OOP) comes in. It's one of the most popular frameworks, and you've likely worked with it in class or otherwise. OOP offers a way to organize code into logical units that make it easier to read, robust, and testable. By structuring our code around objects that encapsulate data and functionality, we create a modular framework that simplifies complex data operations and supports future growth.\n",
-    "\n",
-    "OOP promotes modularity, reusability, and maintainability, allowing for a clearer separation of concerns and reducing the likelihood of errors. Additionally, OOP’s use of abstraction and inheritance helps create reusable components, ensuring consistent and efficient code across all parts of the project. So, what does this all actually look like in practice?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start with a general flow diagram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "flowchart()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Data Reader and Class"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first stage in the process is obviously cleaning and processing the data, and one of the interesting things that you may not have seen in course-work is the segmentation of the data reader class and the dataset class. Often in smaller projects, the reader functionality is added as a class method, many times within the instantiation call, which can make intuitive sense. However, there are a few key benefits to separating them out when we work with larger projects.\n",
-    "\n",
-    "1. Separation of Concerns: By creating separate classes, each class is responsible for a distinct function — the data reader class handles the loading and preprocessing of data from various sources, while the dataset class manages how the data is stored, accessed, and manipulated. This separation makes the codebase easier to understand and modify, as changes to how data is read do not impact how the data is organized or utilized downstream.\n",
-    "\n",
-    "\n",
-    "2.\tReusability: When data reading and data management are isolated into their own classes, they can be reused across different projects or tasks without modification. For instance, the same data reader class can be employed to load data from multiple sources (like CSVs, databases, or APIs) while reusing the dataset class to provide a uniform interface for accessing and manipulating data, regardless of its origin.\n",
-    "3.\tMaintainability: Segmenting these responsibilities simplifies debugging and testing. If there is an issue with data loading, the problem is likely within the data reader class, whereas issues with data handling can be traced back to the dataset class. This modularity reduces the risk of unintended side effects when making changes or improvements, since each class operates independently.\n",
-    "4.\tFlexibility: A segmented approach allows developers to quickly adapt to changes in data sources or formats without needing to rewrite core data handling logic. The data reader class can be updated to handle new data formats or sources, while the dataset class remains consistent in providing a structured interface for data access and manipulation.\n",
-    "5.  Testability: Perhaps one of the largest advantages is a layout that more easily lends itself to testing. If someone manually creates a test set, either class can be tested regardless of the other's state, and where the errors lie is often much easier to figure out\n",
-    "\n",
-    "As you'll come to see, one of the core principles used to develop large projects like this is similar to the idea of a black box from machine learning. With multiple people working together, there is an emphasis on smoothly integrating everyone's work. So this approach can allow a potentially difficult task (loading and preparing the data) to be distributed among developers without requiring everyone to understand all the intricacies of each class\n",
-    "\n",
-    "Let's see how this has been implemented!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import functools\n",
-    "from dataclasses import dataclass, field\n",
-    "from typing import Callable, Tuple\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class Country:\n",
-    "    id: str = field(hash=True)\n",
-    "    continent: str = field(repr=False)\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class Location:\n",
-    "    id: str = field(hash=True)\n",
-    "    address: str = field(repr=False)\n",
-    "    country: Country = field(repr=False)\n",
-    "    latitude: float = field(repr=False)\n",
-    "    longitude: float = field(repr=False)\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class DisasterType:\n",
-    "    id: str\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class DisasterImpact:\n",
-    "    id: str\n",
-    "    disaster: \"Disaster\" = field(repr=False)\n",
-    "    location: \"DisasterLocation\" = field(repr=False)\n",
-    "    sub_location_nr: int = field(repr=False)\n",
-    "    total_affected: int = field(repr=False)\n",
-    "\n",
-    "    def __repr__(self):\n",
-    "        return self.id\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class DisasterLocation(Location):\n",
-    "    def __repr__(self):\n",
-    "        return self.id\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class Disaster:\n",
-    "    id: str\n",
-    "    type: DisasterType = field(repr=False)\n",
-    "    day: int = field(repr=False)\n",
-    "    month: int = field(repr=False)\n",
-    "    year: int = field(repr=False)\n",
-    "    impacted_locations: list[DisasterImpact] = field(hash=False, repr=False)\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class Depot(Location):\n",
-    "    pass\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class Item:\n",
-    "    id: str = field(hash=True)\n",
-    "    weight: float = field(repr=False)  # Metric tons\n",
-    "    volume: float = field(repr=False)  # Cubic metres\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class TransportMode:\n",
-    "    id: str = field(hash=True)\n",
-    "    distance_method: str\n",
-    "    big_m_cost_elim: float\n",
-    "    max_driving_time_cut_above_hrs: float\n",
-    "\n",
-    "\n",
-    "@dataclass(frozen=True)\n",
-    "class DistanceInfo:\n",
-    "    distance: float  # Kilometres\n",
-    "    time: float  # Hours\n",
-    "    cost_per_ton: float  # USD\n",
-    "\n",
-    "DistanceMatrix = dict[Tuple[Location, Location, TransportMode], DistanceInfo]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Dataset class encapsulates all data required for the analysis and optimization tasks. It includes lists of depots, disasters, and disaster_locations, and dictionaries for probabilities of disasters, inventory levels, and other critical data. The _zero_demand_threshold is a constant used internally to determine when demand should be considered negligible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@dataclass(frozen=True)\n",
-    "class Dataset:\n",
-    "    depots: list[Depot]\n",
-    "    disasters: list[Disaster]\n",
-    "    disaster_locations: list[DisasterLocation]\n",
-    "    probabilities: dict[Disaster, float]\n",
-    "    items: list[Item]\n",
-    "    transport_modes: list[TransportMode]\n",
-    "    inventory: dict[Tuple[Depot, Item], int]\n",
-    "    inventory_scenarios: dict[str, dict[Tuple[Depot, Item], int]]\n",
-    "    distance: DistanceMatrix\n",
-    "    people_affected: dict[Tuple[DisasterImpact, Item], float]\n",
-    "    persons_per_item_general: dict[Tuple[DisasterImpact, Item], float]\n",
-    "    persons_per_item_monthly: dict[Tuple[DisasterImpact, Item], float]\n",
-    "    disaster_affected_totals: dict[str, int]\n",
-    "\n",
-    "    _zero_demand_threshold = 1e6\n",
-    "\n",
-    "\n",
-    "    '''\n",
-    "    The take_disaster_subset method allows us to focus our analysis on a specific \n",
-    "    subset of disasters by providing a predicate function that filters disasters \n",
-    "    based on certain criteria (e.g., date range, disaster type). It recalculates \n",
-    "    probabilities to ensure they sum up to 1 after filtering. Related data such as \n",
-    "    locations, distances, and affected populations are also filtered to maintain \n",
-    "    consistency. If no filtering occurs (i.e., all disasters are included), the method \n",
-    "    returns the original dataset to avoid unnecessary duplication.\n",
-    "    '''\n",
-    "\n",
-    "    def take_disaster_subset(self, predicate: Callable[[Disaster], bool]) -> \"Dataset\":\n",
-    "        \"\"\"\n",
-    "        Generate a smaller dataset by only selecting a subset of the disasters with corresponding data\n",
-    "        \"\"\"\n",
-    "        disasters = list(filter(predicate, self.disasters))\n",
-    "\n",
-    "        if len(disasters) == len(self.disasters):\n",
-    "            return self\n",
-    "\n",
-    "        total_probability = sum(self.probabilities[disaster] for disaster in disasters)\n",
-    "        probabilities = {\n",
-    "            disaster: self.probabilities[disaster] / total_probability\n",
-    "            for disaster in disasters\n",
-    "        }\n",
-    "        locations = [\n",
-    "            impact.location\n",
-    "            for disaster in disasters\n",
-    "            for impact in disaster.impacted_locations\n",
-    "        ]\n",
-    "        distance = {\n",
-    "            (source, destination, mode): cell\n",
-    "            for (source, destination, mode), cell in self.distance.items()\n",
-    "            if destination in locations\n",
-    "        }\n",
-    "        people_affected = {\n",
-    "            (location, item): value\n",
-    "            for (location, item), value in self.people_affected.items()\n",
-    "            if location in locations\n",
-    "        }\n",
-    "        persons_per_item_general = {\n",
-    "            (location, item): value\n",
-    "            for (location, item), value in self.persons_per_item_general.items()\n",
-    "            if location in locations\n",
-    "        }\n",
-    "        persons_per_item_monthly = {\n",
-    "            (location, item): value\n",
-    "            for (location, item), value in self.persons_per_item_monthly.items()\n",
-    "            if location in locations\n",
-    "        }\n",
-    "        return Dataset(\n",
-    "            self.depots,\n",
-    "            disasters,\n",
-    "            locations,\n",
-    "            probabilities,\n",
-    "            self.items,\n",
-    "            self.transport_modes,\n",
-    "            self.inventory,\n",
-    "            self.inventory_scenarios,\n",
-    "            distance,\n",
-    "            people_affected,\n",
-    "            persons_per_item_general,\n",
-    "            persons_per_item_monthly,\n",
-    "            self.disaster_affected_totals,\n",
-    "        )\n",
-    "    \n",
-    "    '''\n",
-    "    The take_inventory_scenario method enables us to switch between different \n",
-    "    inventory configurations stored in inventory_scenarios. By specifying a filename \n",
-    "    key, we retrieve the corresponding inventory data. If the requested inventory \n",
-    "    is already in use, the method returns the current dataset. Otherwise, it creates \n",
-    "    a new Dataset instance with the updated inventory, facilitating comparative \n",
-    "    analysis of different supply scenarios.\n",
-    "    '''\n",
-    "\n",
-    "    def take_inventory_scenario(self, filename: str):\n",
-    "        if filename not in self.inventory_scenarios:\n",
-    "            raise RuntimeError(\"Inventory scenario not found\")\n",
-    "        inventory = self.inventory_scenarios[filename]\n",
-    "        if inventory == self.inventory:\n",
-    "            return self\n",
-    "        return Dataset(\n",
-    "            self.depots,\n",
-    "            self.disasters,\n",
-    "            self.disaster_locations,\n",
-    "            self.probabilities,\n",
-    "            self.items,\n",
-    "            self.transport_modes,\n",
-    "            inventory,\n",
-    "            self.inventory_scenarios,\n",
-    "            self.distance,\n",
-    "            self.people_affected,\n",
-    "            self.persons_per_item_general,\n",
-    "            self.persons_per_item_monthly,\n",
-    "            self.disaster_affected_totals,\n",
-    "        )\n",
-    "    '''\n",
-    "    The general_demand property calculates the overall demand for each \n",
-    "    item at each impacted location using general beta values \n",
-    "    (persons_per_item_general). The beta value represents how many people \n",
-    "    can be served per item. We utilize the _calc_items_needed helper method \n",
-    "    for calculations. The @functools.cached_property decorator ensures that \n",
-    "    the result is computed once and cached, improving performance for repeated \n",
-    "    access.\n",
-    "\n",
-    "    \n",
-    "    '''\n",
-    "    @functools.cached_property\n",
-    "    def general_demand(self) -> dict[Tuple[DisasterImpact, Item], float]:\n",
-    "        general_demand = {\n",
-    "            (location, item): self._calc_items_needed(\n",
-    "                self.people_affected[location, item],\n",
-    "                self.persons_per_item_general[location, item],\n",
-    "            )\n",
-    "            for disaster in self.disasters\n",
-    "            for location in disaster.impacted_locations\n",
-    "            for item in self.items\n",
-    "        }\n",
-    "\n",
-    "        return {key: value for key, value in general_demand.items() if value > 1e-1}\n",
-    "\n",
-    "\n",
-    "    '''\n",
-    "    Similarly, the monthly_demand property calculates demand on a monthly \n",
-    "    basis using persons_per_item_monthly. This allows us to capture time-sensitive \n",
-    "    demand variations, which are crucial in disaster response. The result is \n",
-    "    cached, and we filter out values below 1e-3 to maintain computational efficiency.\n",
-    "    '''\n",
-    "\n",
-    "    @functools.cached_property\n",
-    "    def monthly_demand(self) -> dict[Tuple[DisasterImpact, Item], float]:\n",
-    "        monthly_demand = {\n",
-    "            (location, item): self._calc_items_needed(\n",
-    "                self.people_affected[location, item],\n",
-    "                self.persons_per_item_monthly[location, item],\n",
-    "            )\n",
-    "            for disaster in self.disasters\n",
-    "            for location in disaster.impacted_locations\n",
-    "            for item in self.items\n",
-    "        }\n",
-    "\n",
-    "        return {key: value for key, value in monthly_demand.items() if value > 1e-3}\n",
-    "\n",
-    "    '''\n",
-    "    The _calc_items_needed helper method computes the number of items required \n",
-    "    based on the number of people affected and the beta value. If beta is zero or \n",
-    "    exceeds a predefined threshold (_zero_demand_threshold), indicating negligible \n",
-    "    demand, the method returns zero. Otherwise, it divides people_affected by beta \n",
-    "    to determine the required items. This calculation is fundamental for planning supply \n",
-    "    quantities in logistics.\n",
-    "    '''\n",
-    "    \n",
-    "    def _calc_items_needed(\n",
-    "        self,\n",
-    "        people_affected: float,\n",
-    "        beta: float,\n",
-    "    ):\n",
-    "        if beta == 0 or beta >= self._zero_demand_threshold:\n",
-    "            return 0\n",
-    "        else:\n",
-    "            return people_affected / beta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Analysis "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you recall from section 2.6, to solve for optimal allocations we have to consider several independent items, which requires solving the model for each version. This can get messy pretty quickly considering the complexity of the model and the dataset that it uses. So in the actual implementation, the developers created a class to handle this larger loop. It will set up the problem for a specific context, and then call a dedicated solver class. \n",
-    "\n",
-    "While we won't go into it here, this set up also lends itself nicely to leveraging multithreading. You'll notice that there is a sub-class in the code which creates \"workers\". These allow use to handle each problem independently and is a common set-up in python for multi-threading. Luckily for our sakes, the problem overall is fast enough to be solved on a single instance, so this hasn't been implemented fully in production code!\n",
-    "\n",
-    "So let's take a look at how it all comes together.\n",
-    "\n",
-    "Firsr we'll define our name space and methods used from the data reader class:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from enum import IntEnum\n",
-    "from multiprocessing import Pool\n",
-    "from os import getenv\n",
-    "from typing import Callable\n",
-    "\n",
-    "import pandas as pd\n",
-    "\n",
-    "from src.data import Dataset, Disaster, DisasterImpact, DistanceInfo, Item\n",
-    "from src.solving import (\n",
-    "    AllocationStrategy,\n",
-    "    CostMatrix,\n",
-    "    Problem,\n",
-    "    Solution,\n",
-    "    SolverParameters,\n",
-    "    StochasticSolver,\n",
-    ")\n",
-    "\n",
-    "class SolverObjective(IntEnum):\n",
-    "    Cost = (0,)\n",
-    "    Time = (1,)\n",
-    "    Distance = 2\n",
-    "\n",
-    "SolutionTags = tuple[SolverObjective, AllocationStrategy]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we'll get into the meat of it:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, AnalysisParameters is a configuration class that holds various parameters influencing the analysis process. These settings allow us to control aspects like which disasters to consider, the objectives to optimize for, and how to handle inventory and demand."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class AnalysisParameters:\n",
-    "    \"\"\"\n",
-    "\n",
-    "    Attributes\n",
-    "    ----------\n",
-    "    expand_depot_set\n",
-    "        Flag indicating whether inventory can be reallocated to depots that don't currently hold any stock\n",
-    "    care_about_month_demand\n",
-    "        Flag indicating whether we take month-by-month demand (True) or the general number (False)\n",
-    "    disaster_month\n",
-    "        Month from which to select disasters\n",
-    "    num_months_to_average\n",
-    "        Number of months to use for selecting disasters, when disasterMonth>=0\n",
-    "    optimization_objectives\n",
-    "        Set of objectives to use for running the optimization model\n",
-    "    comparison_objectives\n",
-    "        Set of objectives to use for comparing results\n",
-    "    allocation_strategies\n",
-    "        Which strategies to test for (re)allocation inventory to depots in the first stage\n",
-    "    min_year\n",
-    "        First year from which disasters should be taken into account\n",
-    "    max_year\n",
-    "        Last year from which disasters should be taken into account\n",
-    "    scale_demand\n",
-    "        Whether demand must be scaled to not exceed total available inventory or not\n",
-    "\n",
-    "\n",
-    "    \"\"\"\n",
-    "\n",
-    "    expand_depot_set: bool = False\n",
-    "    care_about_month_demand: bool = True\n",
-    "    disaster_month: int = -1\n",
-    "    num_months_to_average: int = 3\n",
-    "    optimization_objectives: list[SolverObjective] = [\n",
-    "        SolverObjective.Cost,\n",
-    "        SolverObjective.Time,\n",
-    "    ]\n",
-    "    comparison_objectives: list[SolverObjective] = list(SolverObjective)\n",
-    "    allocation_strategies: list[AllocationStrategy] = list(AllocationStrategy)\n",
-    "    min_year: int = 1900\n",
-    "    max_year: int = 2100\n",
-    "    scale_demand: bool = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Analysis class encapsulates all results and statistics from the optimization runs. It includes solutions for different objectives and strategies, along with computed metrics that help in evaluating and comparing these solutions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Analysis:\n",
-    "    \"\"\"\n",
-    "    Analysis results for multiple optimization runs for a single dataset and item, using different objectives and allocation strategies.\n",
-    "\n",
-    "    Attributes\n",
-    "    ----------\n",
-    "    parameters:\n",
-    "        Parameters used to construct the analysis\n",
-    "    dataset:\n",
-    "        Original dataset being analyzed\n",
-    "    item:\n",
-    "        Item for which the analysis was performed\n",
-    "    solutions:\n",
-    "        Dictionary of solutions for all solved problems\n",
-    "    solution_stats\n",
-    "        Index: objective, strategy\n",
-    "        Columns:\n",
-    "        - coveredDemandExcDummy\n",
-    "        - dualTotInv\n",
-    "        - totalCostIncDummy\n",
-    "        - totalCostExcDummy\n",
-    "        - totalDemand\n",
-    "        - fractionOfDisastersUsingDummy\n",
-    "        - averageUnitCost\n",
-    "        - demandFulfillmentFraction\n",
-    "    balance_metric\n",
-    "        Index: objective\n",
-    "        Columns: balanceMetric\n",
-    "    units_shipped\n",
-    "        Index: objective, strategy, mode\n",
-    "        Columns: unitsShipped, unitsShippedWeighted\n",
-    "    people_served_per_item\n",
-    "        Index: objective, strategy\n",
-    "        Columns: peopleServedPerItem\n",
-    "    cross_ompact\n",
-    "        Index: objective, strategy, other\n",
-    "        Columns: impact\n",
-    "    \"\"\"\n",
-    "\n",
-    "    parameters: AnalysisParameters\n",
-    "    dataset: Dataset\n",
-    "    item: Item\n",
-    "    solutions: dict[SolutionTags, Solution]\n",
-    "    solution_stats: pd.DataFrame\n",
-    "    balance_metric: pd.DataFrame\n",
-    "    units_shipped: pd.DataFrame\n",
-    "    people_served_per_item: pd.DataFrame\n",
-    "    cross_impact: pd.DataFrame\n",
-    "\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        parameters: AnalysisParameters,\n",
-    "        dataset: Dataset,\n",
-    "        item: Item,\n",
-    "        solutions: dict[SolutionTags, Solution],\n",
-    "        solution_stats: pd.DataFrame,\n",
-    "        balance_metric: pd.DataFrame,\n",
-    "        units_shipped: pd.DataFrame,\n",
-    "        people_served_per_item: pd.DataFrame,\n",
-    "        cross_impact: pd.DataFrame,\n",
-    "    ):\n",
-    "        self.parameters = parameters\n",
-    "        self.dataset = dataset\n",
-    "        self.item = item\n",
-    "        self.solutions = solutions\n",
-    "        self.solution_stats = solution_stats\n",
-    "        self.balance_metric = balance_metric\n",
-    "        self.units_shipped = units_shipped\n",
-    "        self.people_served_per_item = people_served_per_item\n",
-    "        self.cross_impact = cross_impact\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The AnalyzerWorker class performs the core computation. It runs optimization models for different objectives and strategies, processes the results, and computes various metrics for analysis. The _post_process method compiles these results into meaningful statistics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class AnalyzerWorker:\n",
-    "    def __init__(self, parameters: AnalysisParameters):\n",
-    "        self.parameters = parameters\n",
-    "        self._solver = StochasticSolver()\n",
-    "\n",
-    "    def run(self, dataset: Dataset, item: Item) -> Analysis:\n",
-    "        #a=len(str(dataset))\n",
-    "        dataset = self._filter_dataset(dataset)\n",
-    "        #print(len(str(dataset))!=a)\n",
-    "        probabilities = {\n",
-    "            disaster: 1 / len(dataset.disasters) for disaster in dataset.disasters\n",
-    "        }\n",
-    "\n",
-    "        solutions: dict[SolutionTags, Solution] = {}\n",
-    "\n",
-    "        # Construct cost matrices once\n",
-    "        cost_matrices = {\n",
-    "            objective: self._get_cost_matrix(dataset, item, objective)\n",
-    "            for objective in SolverObjective\n",
-    "        }\n",
-    "\n",
-    "        # Construct inventory\n",
-    "        inventory = self._select_inventory(dataset, item)\n",
-    "        if sum(inventory.values()) == 0:\n",
-    "            return None\n",
-    "\n",
-    "        # Construct demand\n",
-    "        demand = self._select_demand(dataset, item)\n",
-    "\n",
-    "        # Solve models for all objectives and strategies\n",
-    "        for objective in self.parameters.optimization_objectives:\n",
-    "            for strategy in self.parameters.allocation_strategies:\n",
-    "                problem = Problem(\n",
-    "                    dataset.depots,\n",
-    "                    inventory,\n",
-    "                    demand,\n",
-    "                    dataset.disasters,\n",
-    "                    probabilities,\n",
-    "                    dataset.transport_modes,\n",
-    "                    cost_matrices[objective],\n",
-    "                )\n",
-    "                parameters = SolverParameters(strategy, self.parameters.scale_demand)\n",
-    "                tags = (objective, strategy)\n",
-    "                solutions[tags] = self._solver.solve(problem, parameters)\n",
-    "\n",
-    "        return self._post_process(dataset, item, cost_matrices, solutions)\n",
-    "\n",
-    "    def dispose(self):\n",
-    "        self._solver.dispose()\n",
-    "\n",
-    "    def _select_inventory(self, dataset: Dataset, item: Item):\n",
-    "        return {\n",
-    "            depot: dataset.inventory.get((depot, item), 0)\n",
-    "            for depot in dataset.depots\n",
-    "            if self.parameters.expand_depot_set\n",
-    "            or dataset.inventory.get((depot, item), 0) > 0\n",
-    "        }\n",
-    "\n",
-    "    def _filter_dataset(self, dataset: Dataset) -> Dataset:\n",
-    "        if self.parameters.disaster_month > -1:\n",
-    "            months = range(\n",
-    "                self.parameters.disaster_month,\n",
-    "                self.parameters.disaster_month\n",
-    "                + 1\n",
-    "                + self.parameters.num_months_to_average,\n",
-    "            )\n",
-    "            months = [(month - 1) % 12 + 1 for month in months]\n",
-    "            predicate: Callable[[Disaster], bool] = (\n",
-    "                lambda disaster: disaster.month in months\n",
-    "            )\n",
-    "            dataset = dataset.take_disaster_subset(predicate)\n",
-    "\n",
-    "        dataset = dataset.take_disaster_subset(\n",
-    "            lambda disaster: disaster.year >= self.parameters.min_year\n",
-    "            and disaster.year <= self.parameters.max_year\n",
-    "        )\n",
-    "\n",
-    "        return dataset\n",
-    "\n",
-    "    def _select_demand(\n",
-    "        self, dataset: Dataset, item: Item\n",
-    "    ) -> dict[DisasterImpact, float]:\n",
-    "        source = (\n",
-    "            dataset.monthly_demand\n",
-    "            if self.parameters.care_about_month_demand\n",
-    "            else dataset.general_demand\n",
-    "        )\n",
-    "        return {\n",
-    "            location: source.get((location, item), 0)\n",
-    "            for disaster in dataset.disasters\n",
-    "            for location in disaster.impacted_locations\n",
-    "        }\n",
-    "\n",
-    "    def _get_cost_matrix(\n",
-    "        self, dataset: Dataset, item: Item, objective: SolverObjective\n",
-    "    ) -> CostMatrix:\n",
-    "        return {\n",
-    "            key: self._get_cost_element(value, objective, item)\n",
-    "            for key, value in dataset.distance.items()\n",
-    "        }\n",
-    "\n",
-    "    def _get_cost_element(\n",
-    "        self, cell: DistanceInfo, objective: SolverObjective, item: Item\n",
-    "    ):\n",
-    "        if objective == SolverObjective.Cost:\n",
-    "            return item.weight * cell.cost_per_ton\n",
-    "        elif objective == SolverObjective.Time:\n",
-    "            return cell.time\n",
-    "        elif objective == SolverObjective.Distance:\n",
-    "            return cell.distance\n",
-    "        else:\n",
-    "            raise RuntimeError(f\"Undefined objective {objective}\")\n",
-    "\n",
-    "    def _post_process(\n",
-    "        self,\n",
-    "        dataset: Dataset,\n",
-    "        item: Item,\n",
-    "        costs: dict[SolverObjective, CostMatrix],\n",
-    "        solutions: dict[SolutionTags, Solution],\n",
-    "    ):\n",
-    "        beta_source = (\n",
-    "            dataset.persons_per_item_monthly\n",
-    "            if self.parameters.care_about_month_demand\n",
-    "            else dataset.persons_per_item_general\n",
-    "        )\n",
-    "        beta = {\n",
-    "            location.id: beta_source[location, item]\n",
-    "            for disaster in dataset.disasters\n",
-    "            for location in disaster.impacted_locations\n",
-    "        }\n",
-    "\n",
-    "        solution_stats = pd.DataFrame.from_records(\n",
-    "            [\n",
-    "                {\n",
-    "                    \"objective\": objective,\n",
-    "                    \"strategy\": strategy,\n",
-    "                    \"coveredDemandExcDummy\": solution.covered_demand_exc_dummy,\n",
-    "                    \"dualTotInv\": solution.dual_total_inventory,\n",
-    "                    \"totalCostIncDummy\": solution.total_cost_inc_dummy,\n",
-    "                    \"totalCostExcDummy\": solution.total_cost_exc_dummy,\n",
-    "                    \"totalDemand\": solution.total_demand,\n",
-    "                    \"fractionOfDisastersUsingDummy\": solution.fraction_of_disasters_using_dummy,\n",
-    "                }\n",
-    "                for (objective, strategy), solution in solutions.items()\n",
-    "            ]\n",
-    "        ).set_index([\"objective\", \"strategy\"])\n",
-    "\n",
-    "        df_flows = pd.DataFrame.from_records(\n",
-    "            [\n",
-    "                {\n",
-    "                    \"objective\": objective,\n",
-    "                    \"strategy\": strategy,\n",
-    "                    \"disaster\": disaster.id,\n",
-    "                    \"depot\": depot.id,\n",
-    "                    \"impact\": impact.id,\n",
-    "                    \"location\": impact.location.id,\n",
-    "                    \"mode\": mode.id,\n",
-    "                    \"flow\": value,\n",
-    "                    \"distance\": dataset.distance[depot, impact.location, mode].distance\n",
-    "                    if depot.id != \"DUMMY\"\n",
-    "                    else None,\n",
-    "                }\n",
-    "                for (objective, strategy), solution in solutions.items()\n",
-    "                for (disaster, depot, impact, mode), value in solution.flow.items()\n",
-    "            ]\n",
-    "        )\n",
-    "\n",
-    "        # Average unit cost\n",
-    "        solution_stats[\"averageUnitCost\"] = solution_stats[\"totalCostExcDummy\"] / (\n",
-    "            solution_stats[\"coveredDemandExcDummy\"] + 1e-7\n",
-    "        )\n",
-    "\n",
-    "        # Demand fulfillment fraction\n",
-    "        solution_stats[\"demandFulfillmentFraction\"] = solution_stats[\n",
-    "            \"coveredDemandExcDummy\"\n",
-    "        ] / (solution_stats[\"totalDemand\"] + 1e-7)\n",
-    "\n",
-    "        # Balance metric\n",
-    "        strategies = set(solution_stats.reset_index()[\"strategy\"])\n",
-    "        pivoted = solution_stats.reset_index().pivot(\n",
-    "            index=\"objective\", columns=\"strategy\", values=\"totalCostExcDummy\"\n",
-    "        )\n",
-    "        pivoted[\"balanceMetric\"] = (\n",
-    "            pivoted[AllocationStrategy.MinimizeFixedInventory]\n",
-    "            / (pivoted[AllocationStrategy.MinimizeTwoStage] + 1e-7)\n",
-    "            if AllocationStrategy.MinimizeFixedInventory in strategies\n",
-    "            and AllocationStrategy.MinimizeTwoStage in strategies\n",
-    "            else None\n",
-    "        )\n",
-    "        balance_metric = pivoted\n",
-    "\n",
-    "        df_probabilities = pd.DataFrame.from_dict(\n",
-    "            {\n",
-    "                disaster.id: dataset.probabilities[disaster]\n",
-    "                for disaster in dataset.disasters\n",
-    "            },\n",
-    "            columns=[\"probability\"],\n",
-    "            orient=\"index\",\n",
-    "        )\n",
-    "\n",
-    "        # Units shipped\n",
-    "        df_flow_no_dummy = df_flows.join(df_probabilities, on=\"disaster\")\n",
-    "        df_flow_no_dummy = df_flow_no_dummy[\n",
-    "            df_flow_no_dummy[\"depot\"] != \"DUMMY\"\n",
-    "        ]  # TODO Replace hardcoded dummy ID\n",
-    "        temp = df_flow_no_dummy.copy()\n",
-    "        temp[\"unitsShipped\"] = temp[\"probability\"] * temp[\"flow\"]\n",
-    "        temp[\"unitsShippedWeighted\"] = temp[\"unitsShipped\"] * temp[\"distance\"]\n",
-    "        units_shipped = (\n",
-    "            temp.set_index([\"objective\", \"strategy\", \"mode\"])[\n",
-    "                [\"unitsShipped\", \"unitsShippedWeighted\"]\n",
-    "            ]\n",
-    "            .groupby([\"objective\", \"strategy\", \"mode\"])\n",
-    "            .sum()\n",
-    "        )\n",
-    "\n",
-    "        # People served per item\n",
-    "        temp = df_flow_no_dummy.copy()\n",
-    "        temp[\"beta\"] = temp[\"impact\"].apply(lambda loc: beta[loc])\n",
-    "        temp[\"peopleServed\"] = temp[\"probability\"] * temp[\"beta\"] * temp[\"flow\"]\n",
-    "        people_served = (\n",
-    "            temp.set_index([\"objective\", \"strategy\"])[\"peopleServed\"]\n",
-    "            .groupby([\"objective\", \"strategy\"])\n",
-    "            .sum()\n",
-    "        )\n",
-    "        people_served_per_item = pd.DataFrame(\n",
-    "            people_served / (solution_stats[\"coveredDemandExcDummy\"] + 1e-7),\n",
-    "            columns=[\"peopleServedPerItem\"],\n",
-    "        )\n",
-    "\n",
-    "        # Impact of optimizing one objective on another objective\n",
-    "        impact = []\n",
-    "        for other in self.parameters.comparison_objectives:\n",
-    "            cost = {\n",
-    "                (depot.id, location.id, mode.id): value\n",
-    "                for (depot, location, mode), value in costs[other].items()\n",
-    "            }\n",
-    "            temp = df_flow_no_dummy.copy()\n",
-    "            if temp.empty:\n",
-    "                raise RuntimeError(\"Empty flow matrix encountered\")\n",
-    "            temp[\"cost\"] = temp.apply(\n",
-    "                lambda row: cost[row[\"depot\"], row[\"location\"], row[\"mode\"]], axis=1\n",
-    "            )\n",
-    "            temp[\"other\"] = other\n",
-    "            temp[\"impact\"] = temp[\"cost\"] * temp[\"probability\"] * temp[\"flow\"]\n",
-    "            impact.append(temp.reset_index())\n",
-    "        cross_impact = (\n",
-    "            pd.concat(impact)\n",
-    "            .groupby([\"objective\", \"strategy\", \"other\"])[[\"impact\"]]\n",
-    "            .sum()\n",
-    "        )\n",
-    "\n",
-    "        return Analysis(\n",
-    "            self.parameters,\n",
-    "            dataset,\n",
-    "            item,\n",
-    "            solutions,\n",
-    "            solution_stats,\n",
-    "            balance_metric,\n",
-    "            units_shipped,\n",
-    "            people_served_per_item,\n",
-    "            cross_impact,\n",
-    "        )\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Analyzer class manages the analysis workflow. It can run analysis for a single item or for all items across various inventory scenarios. It utilizes the AnalyzerWorker to perform the computations and collects the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Analyzer:\n",
-    "    \"\"\"\n",
-    "    Service responsible for performing optimization runs and analysis on the results\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def __init__(self, parameters: AnalysisParameters):\n",
-    "        self.parameters = parameters\n",
-    "\n",
-    "    def run(self, dataset: Dataset, item: Item) -> Analysis:\n",
-    "        worker = AnalyzerWorker(self.parameters)\n",
-    "        result = worker.run(dataset, item)\n",
-    "        worker.dispose()\n",
-    "        return result\n",
-    "\n",
-    "    def run_all(self, dataset: Dataset) -> dict[tuple[str, Item], Analysis]:\n",
-    "        inventory_datasets = {\n",
-    "            filename: dataset.take_inventory_scenario(filename)\n",
-    "            for filename in dataset.inventory_scenarios\n",
-    "        }\n",
-    "        tasks = [\n",
-    "            (filename, inventory_dataset, item)\n",
-    "            for (filename, inventory_dataset) in inventory_datasets.items()\n",
-    "            for item in inventory_dataset.items\n",
-    "        ]\n",
-    "\n",
-    "        return self._run_tasks(tasks)\n",
-    "\n",
-    "    def _run_tasks(self, tasks: list[tuple[str, Dataset, Item]]):\n",
-    "        use_multi_processing = getenv(\"CI\", \"false\") == \"false\"\n",
-    "        use_multi_processing = False\n",
-    "        if use_multi_processing:\n",
-    "            with Pool(\n",
-    "                initializer=_analysis_worker_init, initargs=[self.parameters]\n",
-    "            ) as pool:\n",
-    "                result: list[tuple[str, Analysis]] = pool.map(\n",
-    "                    _analysis_worker_call, tasks\n",
-    "                )\n",
-    "        else:\n",
-    "            worker = AnalyzerWorker(self.parameters)\n",
-    "            result = [\n",
-    "                (filename, worker.run(dataset, item))\n",
-    "                for (filename, dataset, item) in tasks\n",
-    "            ]\n",
-    "            worker.dispose()\n",
-    "        return {\n",
-    "            (filename, analysis.item): analysis\n",
-    "            for (filename, analysis) in result\n",
-    "            if analysis is not None\n",
-    "        }\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These next two functiomns function initializes a global worker for use in multiprocessing scenarios and calls the run method of the global worker with the provided arguments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _analysis_worker_init(parameters):\n",
-    "    global worker\n",
-    "    worker = AnalyzerWorker(parameters)\n",
-    "\n",
-    "\n",
-    "def _analysis_worker_call(arg: tuple[Dataset, Item]) -> tuple[str, Analysis]:\n",
-    "    global worker\n",
-    "    (filename, dataset, item) = arg\n",
-    "    return (filename, worker.run(dataset, item))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Geting it ready for Optimization\n",
-    "\n",
-    "\n",
-    "Let's take a look at thow the data is formated to be used in our solver. First we will set the context of our problemn "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Solver\n",
-    "The loop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Why the Prevalent Use of Dictionaries vs Lists?\n",
-    "Dictionaries are often favored over lists in production code for several reasons:\n",
-    "\n",
-    "-\tKey-Value Pairs: Dictionaries provide a convenient way to store and retrieve data by keys rather than by index. This makes dictionaries especially useful for scenarios where data is logically organized by unique identifiers (e.g., JSON-like structures, configurations).\n",
-    "-\tReadability and Maintainability: With dictionaries, code becomes more readable and self-explanatory. For example, user['name'] is more descriptive than user[0].\n",
-    "-\tFlexibility in Data Manipulation: Unlike lists, dictionaries allow quick updates and deletions by key, improving flexibility when modifying data.\n",
-    "-\tPerformance: For lookups, inserts, and deletions, dictionaries (hash maps) often provide average O(1) time complexity, while lists require O(n) for searching or removing elements unless the position is already known."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dataclasses import dataclass\n",
-    "from enum import IntEnum\n",
-    "from typing import Tuple, Union\n",
-    "\n",
-    "import gurobipy as gp\n",
-    "from gurobipy import GRB, tupledict\n",
-    "\n",
-    "from src.data import Depot, Disaster, DisasterImpact, Location, TransportMode\n",
-    "\n",
-    "\n",
-    "class AllocationStrategy(IntEnum):\n",
-    "    '''\n",
-    "    The AllocationStrategy enumeration defines different strategies for allocating inventory in \n",
-    "    our optimization model\n",
-    "\n",
-    "    MinimizeTwoStage: Allows inventory reallocation in both stages of the model.\n",
-    "\n",
-    "    MinimizeFixedInventory: Keeps the inventory fixed as in the initial state.\n",
-    "\n",
-    "    WorstDepot: Allocates all inventory to the worst-performing depot to test the model's robustness.\n",
-    "    '''\n",
-    "    MinimizeTwoStage = 0\n",
-    "    MinimizeFixedInventory = 1\n",
-    "    WorstDepot = 2\n",
-    "\n",
-    "'''\n",
-    "The SolverParameters class holds configuration settings for the solver.\n",
-    "'''\n",
-    "@dataclass(frozen=True)\n",
-    "class SolverParameters:\n",
-    "    \"\"\"\n",
-    "    Parameters that influence how the solver transforms the Problem into a mathematical model.\n",
-    "\n",
-    "    Attributes\n",
-    "    ----------\n",
-    "    allocation_strategy\n",
-    "        If and how inventory can be reallocated\n",
-    "    scale_demand\n",
-    "        Whether demand should be scaled (down) to not exceed supply\n",
-    "    \"\"\"\n",
-    "\n",
-    "    allocation_strategy: AllocationStrategy = (\n",
-    "        AllocationStrategy.MinimizeFixedInventory,\n",
-    "    )\n",
-    "    scale_demand: bool = False\n",
-    "\n",
-    "'''\n",
-    "We define a type alias CostMatrix for readability. It represents a dictionary mapping a \n",
-    "tuple of (source location, target location, transport mode) to a cost value (e.g., transportation \n",
-    "cost, time, or distance). This matrix is essential for the optimization model to calculate \n",
-    "costs associated with moving goods from depots to disaster impact locations.\n",
-    "'''\n",
-    "CostMatrix = dict[Tuple[Location, Location, TransportMode], float]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@dataclass(frozen=True)\n",
-    "class Problem:\n",
-    "    '''\n",
-    "    The Problem class encapsulates all the data required to define an optimization problem:\n",
-    "\n",
-    "    depots: A list of depots where inventory is stored.\n",
-    "    \n",
-    "    inventory: A dictionary mapping each depot to the quantity of inventory it holds.\n",
-    "    \n",
-    "    demand: A dictionary mapping each DisasterImpact location to its demand.\n",
-    "    \n",
-    "    disasters: A list of disasters being considered.\n",
-    "    \n",
-    "    probabilities: A dictionary mapping each disaster to its probability of occurrence.\n",
-    "    \n",
-    "    transport_modes: A list of available transport modes.\n",
-    "    \n",
-    "    cost: The CostMatrix containing cost information for transporting goods.*\n",
-    "    '''\n",
-    "    depots: list[Depot]\n",
-    "    inventory: dict[Depot, int]\n",
-    "    demand: dict[DisasterImpact, float]\n",
-    "    disasters: list[Disaster]\n",
-    "    probabilities: dict[Disaster, float]\n",
-    "    transport_modes: list[TransportMode]\n",
-    "    cost: CostMatrix"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Solution class stores the results obtained after solving a Problem with specific SolverParameters. It includes various metrics and variables that describe the performance and decisions made by the optimization model, such as total costs, demand coverage, dual variables, and optimal flows. The _dummy_depot is a special depot used internally to handle unmet demand in the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@dataclass(frozen=True)\n",
-    "class Solution:\n",
-    "    \"\"\"\n",
-    "    Result of solving a single Problem with specific SolverParameters\n",
-    "\n",
-    "    Attributes\n",
-    "    ----------\n",
-    "    total_cost_inc_dummy\n",
-    "        Total transportation cost including artificial cost for using the dummy node (myObj)\n",
-    "    total_cost_exc_dummy\n",
-    "        Total transportation cost from the real depots (myObjNoDum)\n",
-    "    total_demand\n",
-    "        Total demand in the input data (myWeightedDemand)\n",
-    "    covered_demand_exc_dummy\n",
-    "        Demand served from real depots, averaged over all scenarios (myWeightedDemandMetNoDum)\n",
-    "    fraction_of_disasters_using_dummy\n",
-    "        Fraction of disaster scenarios for which not enough real inventory is available (myFractionOfDisastersUsingDummy)\n",
-    "    duals_inventory_exc_dummy_plus_dummy_cost\n",
-    "        Adjusted dual variables for the inventory constraints (values are independent of dummy costs) (dualsInvNoDum_PlusDummyCost)\n",
-    "    duals_inventory_exc_dummy_unadjusted\n",
-    "        Original dual variables for the inventory constraints, aggregated over disaster scenarios (dualsInvNoDum_UnAdj)\n",
-    "    duals_inventory_exc_dummy_all\n",
-    "        All original dual variables for the inventory constraints (dualsInvNoDum_All)\n",
-    "    flow_exc_dummy\n",
-    "        Allocation of depot inventory to disaster locations in each scenario, excluding the dummy depot (myFlowNoDum)\n",
-    "    flow\n",
-    "        Allocation of depot inventory to disaster locations in each scenario (myFlow)\n",
-    "    optimal_inventory\n",
-    "        Optimal or fixed allocation of inventory to depots (myOptInvNoDum)\n",
-    "    dual_total_inventory\n",
-    "        Dual variable for the total inventory constraint (dualTotInv)\n",
-    "    \"\"\"\n",
-    "\n",
-    "    total_cost_inc_dummy: float\n",
-    "    total_cost_exc_dummy: float\n",
-    "    total_demand: float\n",
-    "    covered_demand_exc_dummy: float\n",
-    "    fraction_of_disasters_using_dummy: float\n",
-    "    duals_inventory_exc_dummy_plus_dummy_cost: dict[Depot, float]\n",
-    "    duals_inventory_exc_dummy_unadjusted: dict[Depot, float]\n",
-    "    duals_inventory_exc_dummy_all: dict[Tuple[Disaster, Depot], float]\n",
-    "    flow_exc_dummy: dict[Tuple[Disaster, Depot, DisasterImpact, TransportMode], float]\n",
-    "    flow: dict[Tuple[Disaster, Depot, DisasterImpact, TransportMode], float]\n",
-    "    optimal_inventory: dict[Depot, float]\n",
-    "    dual_total_inventory: float\n",
-    "\n",
-    "    _dummy_depot: Depot\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The StochasticSolver class encapsulates the logic for solving stochastic optimization problems. The class variables _threshold_cost_elim and _threshold_cost_dummy are large constants used to effectively eliminate certain arcs or penalize the use of the dummy depot in the optimization model. The constructor initializes a dummy location and creates a Gurobi environment with specific parameters (e.g., disabling output and setting single-threaded execution)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class StochasticSolver:\n",
-    "    _threshold_cost_elim: float = 1e9\n",
-    "    _threshold_cost_dummy: float = 1e9\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        self._dummy = Location(\"DUMMY\", \"\", \"\", 0, 0)\n",
-    "        self._env = gp.Env(params={\"OutputFlag\": 0, \"Threads\": 1})\n",
-    "    '''\n",
-    "    The dispose method releases resources associated with the Gurobi environment. \n",
-    "    It's important to call this method after solving problems to prevent resource \n",
-    "    leaks, especially when solving multiple problems in a loop.\n",
-    "    '''\n",
-    "    def dispose(self):\n",
-    "        self._env.dispose()\n",
-    "        self._env = None\n",
-    "\n",
-    "    '''\n",
-    "    In the solve method, we begin by preparing the data for the optimization model:\n",
-    "    \n",
-    "    We include both real depots and the dummy depot in our list of sources.\n",
-    "    \n",
-    "    Demand scaling is performed if specified in the parameters.\n",
-    "    \n",
-    "    We construct the list of possible arcs in the transportation network, filtering out those with \n",
-    "    prohibitive costs unless they involve the dummy depot.\n",
-    "    \n",
-    "    We calculate the cost for each arc, adjusting for disaster probabilities to reflect the expected\n",
-    "    cost across scenarios.\n",
-    "    \n",
-    "    We initialize a Gurobi model named \"StochLP\" for solving the stochastic linear program\n",
-    "    '''\n",
-    "\n",
-    "    def solve(self, problem: Problem, parameters: SolverParameters) -> Solution:\n",
-    "        sources = problem.depots + [self._dummy]\n",
-    "\n",
-    "        demand = (\n",
-    "            self._scale_demand(problem) if parameters.scale_demand else problem.demand\n",
-    "        )\n",
-    "\n",
-    "        '''\n",
-    "        The arcs represent the possible transportation routes from the supply sources (depots and a dummy depot)\n",
-    "        to the disaster-impacted locations for each disaster scenario and transport mode. Specifically, \n",
-    "        each arc is a tuple (k, i, j, v) where:\n",
-    "\n",
-    "        k i a disaster scenario from the list of disasters being considered.\n",
-    "        \n",
-    "        i is a source location, which can be a real depot or a special dummy depot used to model unmet demand.\n",
-    "        \n",
-    "        j is a disaster impact location, representing an area affected by disaster k where relief goods are needed.\n",
-    "        \n",
-    "        v is a transport mode, such as road, air, or sea transport.\n",
-    "        These arcs form the edges of the transportation network in the model. They define all feasible routes along \n",
-    "        which relief items can be transported from depots to impacted locations under different disaster scenarios and transportation options.\n",
-    "        \n",
-    "        '''\n",
-    "\n",
-    "        arcs = gp.tuplelist(\n",
-    "            [\n",
-    "                (k, i, j, v)\n",
-    "                for i in sources\n",
-    "                for k in problem.disasters\n",
-    "                for j in k.impacted_locations\n",
-    "                for v in problem.transport_modes\n",
-    "                if (\n",
-    "                    self._get_arc_cost(problem.cost, i, j, v)\n",
-    "                    < self._threshold_cost_elim\n",
-    "                )\n",
-    "                or (i == self._dummy)\n",
-    "            ]\n",
-    "        )\n",
-    "\n",
-    "        arc_cost = {\n",
-    "            (k, i, j, v): self._get_arc_cost(problem.cost, i, j, v)\n",
-    "            * problem.probabilities[k]\n",
-    "            for (k, i, j, v) in arcs\n",
-    "        }\n",
-    "\n",
-    "        model = gp.Model(\"StochLP\", env=self._env)\n",
-    "\n",
-    "        # First stage variable: Quantity to be allocated to each depot\n",
-    "        x: tupledict[Depot, gp.Var] = model.addVars(problem.depots, lb=0, name=\"x\")\n",
-    "\n",
-    "        # Second stage variable: Quantity transported from (real or dummy) depot to disaster locations using each mode of transport\n",
-    "        y: tupledict[\n",
-    "            Tuple[Disaster, Union[Depot, Location], DisasterImpact, TransportMode],\n",
-    "            gp.Var,\n",
-    "        ] = model.addVars(arcs, lb=0, obj=arc_cost, name=\"y\")\n",
-    "\n",
-    "        # Constraint: Total incoming arc flow must cover demand for each disaster location\n",
-    "        model.addConstrs(\n",
-    "            (\n",
-    "                y.sum(k, \"*\", j, \"*\") == demand[j]\n",
-    "                for k in problem.disasters\n",
-    "                for j in k.impacted_locations\n",
-    "            ),\n",
-    "            name=\"satisfyDemand\",\n",
-    "        )\n",
-    "\n",
-    "        # Constraint: Total outgoing arc flow must match initial or reallocated inventory\n",
-    "        inventory_balance: tupledict[\n",
-    "            Tuple[Disaster, Depot], gp.Constr\n",
-    "        ] = model.addConstrs(\n",
-    "            (\n",
-    "                y.sum(k, i, \"*\", \"*\") <= x[i]\n",
-    "                for k in problem.disasters\n",
-    "                for i in problem.depots\n",
-    "            ),\n",
-    "            name=\"satisfySupply\",\n",
-    "        )\n",
-    "\n",
-    "        # Constraint: Ensure inventory reallocation matches total existing inventory\n",
-    "        total_initial_inventory = sum(problem.inventory.values())\n",
-    "        match_total_inventory = model.addConstr(x.sum() == total_initial_inventory)\n",
-    "\n",
-    "        '''\n",
-    "        We define a helper function fix_inventory_balance to fix the inventory variables (x) to specific values, \n",
-    "        effectively turning them into constants in the model. Based on the allocation_strategy\n",
-    "        \n",
-    "        MinimizeFixedInventory: We fix the inventory variables to their initial values, preventing reallocation.\n",
-    "        \n",
-    "        WorstDepot: We iterate over all depots, allocating all inventory to each one individually to find the depot \n",
-    "        that results in the worst (highest) objective value. We then fix the inventory allocation to that depot, \n",
-    "        which is useful for stress-testing the model's performance under adverse conditions.\n",
-    "        '''\n",
-    "        def fix_inventory_balance(values: dict[Disaster, float]):\n",
-    "            for key, value in values.items():\n",
-    "                x[key].LB = x[key].UB = value\n",
-    "\n",
-    "        if parameters.allocation_strategy == AllocationStrategy.MinimizeFixedInventory:\n",
-    "            fix_inventory_balance(problem.inventory)\n",
-    "        elif parameters.allocation_strategy == AllocationStrategy.WorstDepot:\n",
-    "            worst_depot = None\n",
-    "            worst_objective = -1e100\n",
-    "            for depot in problem.depots:\n",
-    "                centralized_inventory = {\n",
-    "                    other: total_initial_inventory if other == depot else 0\n",
-    "                    for other in problem.depots\n",
-    "                }\n",
-    "                fix_inventory_balance(centralized_inventory)\n",
-    "                model.optimize()\n",
-    "                if model.Status != GRB.Status.OPTIMAL:\n",
-    "                    raise RuntimeError(\"Could not solve model to optimality\")\n",
-    "                if model.ObjVal > worst_objective:\n",
-    "                    worst_depot = depot\n",
-    "                    worst_objective = model.ObjVal\n",
-    "            centralized_inventory = {\n",
-    "                other: total_initial_inventory if other == worst_depot else 0\n",
-    "                for other in problem.depots\n",
-    "            }\n",
-    "            fix_inventory_balance(centralized_inventory)\n",
-    "\n",
-    "        model.optimize()\n",
-    "        if model.Status != GRB.Status.OPTIMAL:\n",
-    "            raise RuntimeError(\"Could not solve model to optimality\")\n",
-    "\n",
-    "        # Total transport cost\n",
-    "        total_cost_inc_dummy = model.ObjVal\n",
-    "        dummy_cost = sum(\n",
-    "            var.X * var.Obj for var in y.select(\"*\", self._dummy, \"*\", \"*\")\n",
-    "        )\n",
-    "        total_cost_exc_dummy = total_cost_inc_dummy - dummy_cost\n",
-    "\n",
-    "        # Demand met without using the dummy node\n",
-    "        covered_demand_by_dummy = sum(\n",
-    "            y[k, i, j, v].X * problem.probabilities[k]\n",
-    "            for (k, i, j, v) in arcs.select(\"*\", self._dummy, \"*\", \"*\")\n",
-    "        )\n",
-    "        total_demand = sum(\n",
-    "            local_demand * problem.probabilities[j.disaster]\n",
-    "            for (j, local_demand) in demand.items()\n",
-    "        )\n",
-    "        covered_demand_exc_dummy = total_demand - covered_demand_by_dummy\n",
-    "\n",
-    "        # Flow in solution\n",
-    "        solution_y = {key: y[key].X for key in arcs}\n",
-    "\n",
-    "        # Fraction of disaster scenarios in which the dummy supply is used\n",
-    "        fraction_of_disasters_using_dummy = len(\n",
-    "            [\n",
-    "                disaster\n",
-    "                for disaster in problem.disasters\n",
-    "                if sum(\n",
-    "                    solution_y[key]\n",
-    "                    for key in arcs.select(disaster, self._dummy, \"*\", \"*\")\n",
-    "                )\n",
-    "                > 0\n",
-    "            ]\n",
-    "        ) / len(problem.disasters)\n",
-    "\n",
-    "        # Dual variables for the inventory balance constraints\n",
-    "        dual_correction = fraction_of_disasters_using_dummy * self._threshold_cost_dummy\n",
-    "        duals_inventory_exc_dummy_unadjusted = {\n",
-    "            i: sum(inventory_balance[k, i].Pi for k in problem.disasters)\n",
-    "            for i in problem.depots\n",
-    "        }\n",
-    "        duals_inventory_exc_dummy_plus_dummy_cost = {\n",
-    "            i: dual_correction + pi\n",
-    "            for (i, pi) in duals_inventory_exc_dummy_unadjusted.items()\n",
-    "        }\n",
-    "        duals_inventory_exc_dummy_all = {\n",
-    "            (k, i): constr.Pi for (k, i), constr in inventory_balance.items()\n",
-    "        }\n",
-    "\n",
-    "        flow = {(k, i, j, v): var.X for (k, i, j, v), var in y.items() if var.X > 0}\n",
-    "\n",
-    "        flow_exc_dummy = {\n",
-    "            (k, i, j, v): value\n",
-    "            for (k, i, j, v), value in flow.items()\n",
-    "            if i != self._dummy\n",
-    "        }\n",
-    "\n",
-    "        optimal_inventory = {depot: var.X for depot, var in x.items()}\n",
-    "\n",
-    "        dual_total_inventory = (\n",
-    "            match_total_inventory.Pi\n",
-    "            if parameters.allocation_strategy\n",
-    "            == AllocationStrategy.MinimizeFixedInventory\n",
-    "            else None\n",
-    "        )\n",
-    "\n",
-    "        '''\n",
-    "        Finally, we package all the extracted metrics and variables into a Solution object and return it. This object \n",
-    "        provides a comprehensive view of the optimization results, enabling further analysis or reporting.\n",
-    "        '''\n",
-    "\n",
-    "        return Solution(\n",
-    "            total_cost_inc_dummy,\n",
-    "            total_cost_exc_dummy,\n",
-    "            total_demand,\n",
-    "            covered_demand_exc_dummy,\n",
-    "            fraction_of_disasters_using_dummy,\n",
-    "            duals_inventory_exc_dummy_plus_dummy_cost,\n",
-    "            duals_inventory_exc_dummy_unadjusted,\n",
-    "            duals_inventory_exc_dummy_all,\n",
-    "            flow_exc_dummy,\n",
-    "            flow,\n",
-    "            optimal_inventory,\n",
-    "            dual_total_inventory,\n",
-    "            self._dummy,\n",
-    "        )\n",
-    "\n",
-    "    '''\n",
-    "    The _get_arc_cost method retrieves the cost of transporting goods from a source to a target using a specific transport mode. If the \n",
-    "    source is the dummy depot, it returns a high dummy cost to penalize \n",
-    "    its use. If there is no cost information available for a given arc \n",
-    "    (i.e., cell is None), it returns a high elimination threshold cost \n",
-    "    to effectively remove that arc from consideration.\n",
-    "    '''\n",
-    "\n",
-    "    def _get_arc_cost(\n",
-    "        self,\n",
-    "        cost: CostMatrix,\n",
-    "        source: Location,\n",
-    "        target: DisasterImpact,\n",
-    "        mode: TransportMode,\n",
-    "    ):\n",
-    "        if source == self._dummy:\n",
-    "            return self._threshold_cost_dummy\n",
-    "        cell = cost.get((source, target.location, mode))\n",
-    "        return self._threshold_cost_elim if cell == None else cell\n",
-    "\n",
-    "    '''\n",
-    "    The _scale_demand method adjusts the demand levels so that total demand \n",
-    "    does not exceed total supply. For each disaster, it calculates a scaling \n",
-    "    factor based on the ratio of total supply to total demand. It then applies \n",
-    "    this factor to the demand at each impacted location. This is useful in scenarios \n",
-    "    where supply constraints are tight, ensuring that the optimization model \n",
-    "    operates within feasible limits.\n",
-    "    \n",
-    "    '''\n",
-    "    def _scale_demand(self, problem: Problem) -> dict[DisasterImpact, float]:\n",
-    "        supply = sum(problem.inventory.values())\n",
-    "        result: dict[DisasterImpact, float] = {}\n",
-    "        for disaster in problem.disasters:\n",
-    "            total_demand = sum(\n",
-    "                problem.demand[location] for location in disaster.impacted_locations\n",
-    "            )\n",
-    "            factor = min(1, supply / total_demand) if total_demand > 1e-6 else 1\n",
-    "            for location in disaster.impacted_locations:\n",
-    "                result[location] = factor * problem.demand[location]\n",
-    "        return result\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After diving into the code, it's clear that it's exceptionally well-crafted, following best practices and maintaining high readability. The lead developer shared some key insights that contributed to this level of quality. These takeaways not only explain how the code reached its current state but also offer valuable lessons for future projects. Let's explore these insights to understand what makes the code stand out."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1. **Balancing Commenting on Different Projects with Different People Involved**\n",
-    "\n",
-    "\tWhen working on different projects involving different teams, it is crucial to adapt your commenting style to meet the team’s specific needs and coding standards. Here are some tips for balancing commenting styles across projects:\n",
-    "\n",
-    "\t- Understand Team Preferences: Some teams prefer more verbose comments to ensure all members understand the code, while others might lean towards minimal commenting to keep code clean. Spend time understanding each team’s culture and coding standards.\n",
-    "\n",
-    "\t\n",
-    "\t- Project-specific Documentation: Tailor the level and style of commenting to fit the project’s needs. For example, a data science team might require detailed explanations of algorithms and mathematical steps, while a software engineering team may focus more on architectural and functional documentation.\n",
-    "\t-\tReusable Templates: Create reusable templates or guidelines for comments that can be adapted easily for each project. For instance, standardize on docstring formats (e.g., Google-style, reStructuredText) and comment structures that can be tweaked as per the project’s requirements.\n",
-    "\t-\tConsistency is Key: Regardless of the project, maintain consistency in your commenting style within a single codebase. This reduces cognitive load and makes it easier for team members to understand the code quickly.\n",
-    "\t-\tCommunicate Clearly: Have an open discussion with your teammates about their preferences. Clearly defined comment guidelines should be set and documented at the beginning of each project.\n",
-    "\n",
-    "2. **Why the Prevalent Use of Dictionaries vs Lists?**\n",
-    "\tIn the provided code examples, dictionaries are extensively used over lists, and this choice offers several advantages:\n",
-    "\n",
-    "\t-\t**Key-Value Access**: Dictionaries allow for fast retrieval of data using keys, which is essential when dealing with entities identified by unique attributes. For example, the Dataset class uses dictionaries like inventory, probabilities, and distance to map complex relationships between depots, disasters, and locations. \n",
-    "\t\t```python\n",
-    "\t\tinventory: dict[Tuple[Depot, Item], int]\n",
-    "\t\tDistanceMatrix: Dict[Tuple[Location, Location, TransportMode], DistanceInfo]\n",
-    "\t\t```\n",
-    "\n",
-    "\t-   **Readability**: Using dictionaries makes the code more self-explanatory. Accessing ```inventory[(depot, item)]``` is clearer than using index-based access in a list, improving code readability.\n",
-    "\n",
-    "\t-\t**Performance**: Dictionaries provide average O(1) time complexity for lookups, which is beneficial when working with large datasets, as is common in disaster logistics modeling.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "3. **Testing and Debugging**\n",
-    "\n",
-    "\tTesting and debugging are crucial for ensuring the reliability of production code, especially in complex systems like our stochastic solver where very real world impacts can occur if bugs go un-noticed. We haven't shown them specifically here for brevity, however, let's take a look at how we change our code to accomidate them:\n",
-    "\n",
-    "\t-\t**Error Handling**: In the StochasticSolver class, we include checks after optimization to ensure that the model has reached an optimal solution. If not, we raise a RuntimeError with a clear message. For example:\n",
-    "\n",
-    "\t\t```python\n",
-    "\t\tmodel.optimize()\n",
-    "\t\tif model.Status != GRB.Status.OPTIMAL:\n",
-    "\t\t\traise RuntimeError(\"Could not solve model to optimality\")\n",
-    "\t\t```\n",
-    "\t-\t**Modularity for Testing**: The code is organized into modular functions and methods, such as _get_arc_cost and _scale_demand, which makes unit testing more manageable. Each function can be tested independently to verify that it behaves as expected.\n",
-    "\n",
-    "\t\t-\t**Unit Tests**: Write unit tests for every function or module to ensure that each part works as expected independently. Unit tests should be automated and run frequently to catch errors early.\n",
-    "\t\t-\t**Integration Tests**: Test interactions between different modules to catch integration-related issues. Use mocking or stubbing to isolate dependencies when needed.\n",
-    "\t\n",
-    "\t\t-\t**Logging**: Make effective use of logging with different levels (DEBUG, INFO, WARNING, ERROR, CRITICAL). Logging provides a way to understand what the code was doing at the time an error occurred without having to run it in a debugging mode.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "4. **Any Particular Naming Rules? Discuss with Others**\n",
-    "\n",
-    "\tConsistent and meaningful naming conventions are crucial for collaboration:\n",
-    "\n",
-    "\t-\t**CamelCase vs. snake_case**: Choose a naming convention that fits the projectand stick with it throughout the codebase. In this case we have:\n",
-    "\t\t- CamelCase for classes like ```DisasterImpact```, ```AnalyzerWorker```, and ```StochasticSolver```\n",
-    "\t\t-\tsnake_case for variables and functions like ```total_cost_inc_dummy```, ```_filter_dataset```, and ```optimal_inventory``` ) \n",
-    "\n",
-    "\t-\t**Descriptive Names**: Use descriptive names that clearly indicate the purpose of a variable, function, or class. Avoid abbreviations unless they are standard and well-known.\n",
-    "\t\n",
-    "\t-\t**Prefixed Naming**: When needed, prefix variables to indicate their type or usage (is_ for booleans, num_ for numbers, str_ for strings). This is more common in languages with weaker type systems.\n",
-    "\n",
-    "\t\n",
-    "\t-\t**Team Agreements**: Have a discussion with your team to establish a naming convention that everyone agrees on. Document these conventions in a style guide that is accessible to all team members.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "5. **Error Handling**\n",
-    "\n",
-    "\tProper error handling is vital to ensure robustness in production code:\n",
-    "\t-\t**Specific Exceptions**: The code raises specific exceptions with informative messages. For example, in the ```take_inventory_scenario``` method of the Dataset class, a RuntimeError is raised if an inventory scenario is not found\n",
-    "\t\t```python\n",
-    "\t\tif filename not in self.inventory_scenarios:\n",
-    "\t\traise RuntimeError(\"Inventory scenario not found\")\n",
-    "\t\t```\n",
-    "\t-\t**Fallback Mechanisms**: In the solver, when a solution is not optimal, the code doesn't proceed blindly but instead stops execution, which prevents cascading errors and makes debugging easier.\n",
-    "\t-\t**Validation Checks**: Before performing operations, the code often checks for potential issues, such as verifying that the total inventory is not zero before proceeding with the optimization."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "6. **Global Variables**\n",
-    "\n",
-    "\tGlobal variables should generally be avoided in production code due to potential issues with maintainability and concurrency:\n",
-    "\t-\t**Encapsulation**:Encapsulate variables within classes or functions to limit their scope and prevent unintended modifications. Classes like ```AnalyzerWorker``` and ```StochasticSolver``` encapsulate their data and methods, preventing external interference.\n",
-    "\t-\t**Controlled Scope**: In multiprocessing scenarios, global variables are used judiciously. For instance, a global worker is initialized in the ```_analysis_worker_init``` function for use in worker processes, but its scope is limited and managed carefully.\n",
-    "\t\t```python\n",
-    "\t\tdef _analysis_worker_init(parameters):\n",
-    "\t\t\tglobal worker\n",
-    "\t\t\tworker = AnalyzerWorker(parameters)\n",
-    "\n",
-    "\t\t```\n",
-    "\t-\t**Unintended Side Effects**: Global variables can lead to unexpected side effects, making the code harder to debug and maintain.\n",
-    "\t-\t**Thread Safety**: In multi-threaded applications, global variables can lead to race conditions if not handled properly.\n",
-    "\t-\t**Use Constants Judiciously**: If you must use global variables, restrict them to constants (immutable values) and use them sparingly.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "7. **Typing**\n",
-    "\n",
-    "\tStrong typing adds robustness and clarity to production code:\n",
-    "\n",
-    "\t-\t**Function Annotations**: Methods specify the types of their arguments and return values, which aids in understanding what kinds of data are expected.\n",
-    "\t\t```python\n",
-    "\t\tdef _get_cost_element(\n",
-    "\t\t\tself, cell: DistanceInfo, objective: SolverObjective, item: Item):\n",
-    "\t\t```\n",
-    "\n",
-    "\n",
-    "\t-\t**Type Aliases**: Defining type aliases like CostMatrix and DistanceMatrix makes complex types more readable and maintainable.\n",
-    "\t\t```python\n",
-    "\t\tCostMatrix = dict[Tuple[Location, Location, TransportMode], float]\n",
-    "\t\t```\n",
-    "\t-\t**Static Type Checking**: Using type hints allows tools like mypy to perform static type checking, catching potential type errors before runtime.\n",
-    "\n",
-    "\n",
-    "\n",
-    "By following these principles, you can improve code quality, collaboration, and maintainability in a production environment, while also tailoring your practices to different projects and teams."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Runing it All\n",
-    "\n",
-    "So what would runing all this look like? Lets take a look!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "COUNTRY = \"vanuatu_simple\"\n",
-    "\n",
-    "# Run optimization\n",
-    "reader = CsvProblemReader()\n",
-    "dataset = reader.read(DATA_DIR / COUNTRY)\n",
-    "\n",
-    "parameters = AnalysisParameters()\n",
-    "analyzer = Analyzer(parameters)\n",
-    "result = analyzer.run_all(dataset)\n",
-    "\n",
-    "exisiting_stock_df = calc_exisiting_stock_df(dataset, country=COUNTRY)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's save the outputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "WORKSPACE_DIR = Path.cwd().resolve()\n",
-    "DATA_DIR = WORKSPACE_DIR / \"data\"\n",
-    "TEST_DATA_DIR = WORKSPACE_DIR / \"data\" / \"test_data\"\n",
-    "DASHBOARD_OUTPUT_PATH = WORKSPACE_DIR / \"dashboard_output\"\n",
-    "\n",
-    "\n",
-    "priority_change_df = create_priority_change(result, country=COUNTRY)\n",
-    "\n",
-    "all_duals_df = pd.DataFrame()\n",
-    "for key, value in result.items():\n",
-    "    scenario = get_scenario(key)\n",
-    "\n",
-    "    if scenario != \"actual\":\n",
-    "        continue\n",
-    "\n",
-    "    duals_df = calc_duals_by_warehouse(value)\n",
-    "    all_duals_df = pd.concat([all_duals_df, duals_df], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_metrics():\n",
-    "    ### ProvinceAssessDF dashboard file\n",
-    "    provinces_df = pd.read_csv(DATA_DIR / COUNTRY / \"province_lookup.csv\")\n",
-    "    provinces_df: pd.DataFrame = ProvinceLookupDF.validate(provinces_df)\n",
-    "    province_assess_df: pd.DataFrame = create_province_assess_df(\n",
-    "        all_duals_df,\n",
-    "        provinces_df,\n",
-    "        COUNTRY,\n",
-    "    )\n",
-    "\n",
-    "    ### WhStockAssessDF dashboard file\n",
-    "    duals_df_copy = all_duals_df.copy()\n",
-    "    wh_stock_assess: pd.DataFrame = item_stock_assess(duals_df_copy, provinces_df, COUNTRY)\n",
-    "\n",
-    "\n",
-    "    ### Reallocation dashboard file\n",
-    "    reallocation_df, single_warehouse_df = reallocation_dashboard_files(\n",
-    "        wh_stock_assess.copy(),\n",
-    "        result,\n",
-    "        COUNTRY,\n",
-    "        wh_stock_assess=wh_stock_assess,\n",
-    "    )\n",
-    "\n",
-    "    ### Disaster totals dashboard file\n",
-    "    dis_totals_df = create_disaster_totals(dataset, COUNTRY)\n",
-    "\n",
-    "\n",
-    "    ### Save dashboard files\n",
-    "\n",
-    "    if not (DASHBOARD_OUTPUT_PATH / COUNTRY).exists():\n",
-    "        (DASHBOARD_OUTPUT_PATH / COUNTRY).mkdir(parents=True)\n",
-    "\n",
-    "    exisiting_stock_df.to_csv(\n",
-    "        DASHBOARD_OUTPUT_PATH / COUNTRY / \"exisiting_stock.csv\",\n",
-    "        index=False,\n",
-    "    )\n",
-    "\n",
-    "    priority_change_df = priority_change_df.loc[\n",
-    "        priority_change_df[BalMetricsDashboard.run_pct] == \"actual\"\n",
-    "    ]\n",
-    "    priority_change_df = priority_change_df.drop(columns=[BalMetricsDashboard.run_pct])\n",
-    "    priority_change_df.to_csv(\n",
-    "        DASHBOARD_OUTPUT_PATH / COUNTRY / \"priority_change.csv\",\n",
-    "        index=False,\n",
-    "    )\n",
-    "\n",
-    "    province_assess_df = province_assess_df.drop(columns=[ProvinceAssessDF.time])\n",
-    "    province_assess_df.to_csv(\n",
-    "        DASHBOARD_OUTPUT_PATH / COUNTRY / \"province_assess.csv\",\n",
-    "        index=False,\n",
-    "    )\n",
-    "\n",
-    "    dec_wh_stock_assess = wh_stock_assess.drop(columns=[ItemProvinceAssessDF.time_hms])\n",
-    "    dec_wh_stock_assess.to_csv(\n",
-    "        DASHBOARD_OUTPUT_PATH / COUNTRY / \"wh_stock_assess_as_decimal.csv\",\n",
-    "        index=False,\n",
-    "    )\n",
-    "    wh_stock_assess = wh_stock_assess.drop(columns=[ItemProvinceAssessDF.time_hms])\n",
-    "    wh_stock_assess.to_csv(\n",
-    "        DASHBOARD_OUTPUT_PATH / COUNTRY / \"wh_stock_assess.csv\",\n",
-    "        index=False,\n",
-    "    )\n",
-    "\n",
-    "    reallocation_df.to_csv(\n",
-    "        DASHBOARD_OUTPUT_PATH / COUNTRY / \"reallocation.csv\",\n",
-    "        index=False,\n",
-    "    )\n",
-    "    single_warehouse_df.to_csv(\n",
-    "        DASHBOARD_OUTPUT_PATH / COUNTRY / \"single_warehouse.csv\",\n",
-    "        index=False,\n",
-    "    )\n",
-    "\n",
-    "    dis_totals_df.to_csv(\n",
-    "        DASHBOARD_OUTPUT_PATH / COUNTRY / \"disaster_totals.csv\",\n",
-    "        index=False,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Wrapping It Up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we conclude this journey through the evolution of ESUPS and the STOCKHOLM platform, it's inspiring to reflect on how a collaborative spirit and a shared vision can drive meaningful change. ESUPS began as a modest initiative, a simple spreadsheet aimed at cataloging disaster relief supplies to prevent the inefficiencies witnessed during the Nepal earthquake response. However, it was through strategic partnerships with academia, non-profit organizations, and industry leaders that ESUPS transformed into a global force reshaping disaster preparedness and response.\n",
-    "\n",
-    "The collaboration with universities like Penn State and MIT infused ESUPS with cutting-edge research and fresh perspectives. Students and academics brought innovative optimization models and data analytics techniques to the table, enhancing the platform's ability to make data-driven decisions. These partnerships not only advanced ESUPS's mission but also provided invaluable real-world experience for students eager to apply their skills to pressing global challenges.\n",
-    "\n",
-    "Non-profit organizations and NGOs contributed on-the-ground insights and a deep understanding of humanitarian needs. Their involvement ensured that the solutions developed were practical, culturally sensitive, and aligned with the realities of disaster-stricken areas. This synergy between theoretical models and practical application exemplifies how diverse stakeholders can come together to tackle complex problems effectively.\n",
-    "\n",
-    "For students and aspiring professionals, the story of ESUPS serves as a powerful reminder that impactful ideas often start small but can grow exponentially through collaboration and determination. It highlights the potential each individual has to contribute to a better world, especially when leveraging skills in data science, optimization, and technology.\n",
-    "\n",
-    "You, too, can implement your own ideas for a better world. Whether it's developing innovative algorithms to optimize resource allocation, creating platforms that enhance communication among aid organizations, or initiating community projects that address local issues, your contributions matter. The tools and techniques explored in this notebook are not just academic exercises—they are gateways to real-world applications that can save lives and improve outcomes in critical situations.\n",
-    "\n",
-    "We encourage you to take the lessons learned from ESUPS and apply them in your own pursuits:\n",
-    "\n",
-    "-   Embrace Collaboration: Seek partnerships with universities, non-profits, and industry experts. Collaboration amplifies impact.\n",
-    "-   Leverage Technology for Good: Use your technical skills to develop solutions that address societal challenges.\n",
-    "-   Think Globally, Act Locally: Start with problems you are passionate about in your community; small changes can lead to significant impact.\n",
-    "-   Stay Curious and Innovative: Continuously explore new ideas and approaches. Innovation thrives on curiosity and the willingness to challenge the status quo.\n",
-    "By following in the footsteps of ESUPS and other pioneering initiatives, you can be part of a generation that not only understands the complexities of global challenges but actively contributes to solving them. The future of disaster response, humanitarian aid, and many other fields depends on innovative thinkers and dedicated doers like you.\n",
-    "\n",
-    "Remember, significant change often begins with a single idea and grows through collaboration, perseverance, and a shared commitment to making the world a better place. We look forward to seeing how you will apply these insights to create your own impactful narratives."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Math Addendum"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Introduction to Optimization "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### What Happened to the Second Derivative?\n",
-    "\n",
-    "In the realm of optimization, the second derivative of a function, often referred to as the Hessian matrix in multi-dimensional spaces, provides valuable insights into the curvature of the objective function. Specifically, it helps us understand how the slope of the function changes, enabling us to distinguish between local maxima, minima, and saddle points. This information is crucial for methods like Newton's method, which uses the second derivative to make precise adjustments toward the optimal solution.\n",
-    "\n",
-    "However, in complex, real-world problems such as disaster relief logistics, relying on the second derivative presents significant challenges:\n",
-    "\n",
-    "1. **Non-Linear and Non-Smooth Functions**: The objective functions in disaster relief are often non-linear and may have discontinuities due to the various constraints and sudden changes in variables (e.g., supply chain disruptions, varying demand). Calculating the second derivative for such functions can be impractical or impossible, as these irregularities disrupt the smooth curvature that the second derivative relies on.\n",
-    "\n",
-    "2. **High Dimensionality**: The optimization problems in disaster relief involve numerous variables, such as quantities of different supplies, multiple transportation routes, and varying time constraints. The Hessian matrix, representing the second derivatives with respect to all pairs of variables, becomes exceedingly large and computationally expensive to compute and store. In high-dimensional spaces, the complexity and cost of computing the Hessian can outweigh its benefits.\n",
-    "\n",
-    "3. **Dynamic and Stochastic Environments**: The environment in which disaster relief operations take place is dynamic and often stochastic, with elements of uncertainty and unpredictability. These factors introduce variability that is difficult to capture with static second-order information. As the situation evolves, the second derivative may no longer accurately represent the current state of the system.\n",
-    "\n",
-    "4. **Local Optima**: In complex optimization landscapes, there are often multiple local optima. The second derivative provides local information and might lead optimization algorithms to converge to these local optima rather than the global optimum. This is particularly problematic in disaster relief, where finding the best possible solution can make a critical difference.\n",
-    "\n",
-    "5. **Lack of Closed-Form Solutions**: When dealing with high-power polynomials and other complex functions, closed-form solutions for finding the roots do not always exist. The second derivative may help approximate solutions in simpler cases, but as the degree of the polynomial increases, finding exact solutions becomes intractable. This is particularly relevant in disaster logistics, where the relationships between variables can be modeled by high-degree polynomials without straightforward solutions.\n",
-    "\n",
-    "Given these challenges, alternative optimization methods are preferred. Gradient-based approaches like gradient descent rely solely on the first derivative (gradient) and can handle larger, non-linear, and non-smooth problems more effectively. Additionally, heuristic and metaheuristic methods, such as genetic algorithms, simulated annealing, and particle swarm optimization, do not rely on derivative information at all. These methods explore the solution space more broadly and can escape local optima."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### The Largrangian\n",
-    "\n",
-    "The Lagrangian is a fundamental concept in optimization, especially when dealing with constraints. In simple terms, the Lagrangian function incorporates both the original objective function and the constraints of the problem, allowing us to solve constrained optimization problems more effectively. For disaster relief optimization, the Lagrangian helps in balancing the trade-off between minimizing costs and meeting the demand for supplies under various constraints, such as limited resources or maximum allowable transit times. By introducing Lagrange multipliers, we transform the problem into an unconstrained one, making it easier to find optimal solutions that respect all necessary conditions.\n",
-    "\n",
-    "The Lagrangian approach is particularly powerful when second-order methods are impractical due to the reasons mentioned above. By converting a constrained problem into an unconstrained one, it simplifies the complexity and allows for the use of gradient-based methods or other optimization techniques that do not rely on second-order derivatives. This flexibility makes the Lagrangian method a crucial tool in the optimization toolbox, especially for complex, real-world problems like disaster relief logistics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Branch and Bound"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The **Branch and Bound** method is a powerful optimization technique used for solving **integer programming** problems where some or all decision variables must take on integer values. The fundamental idea is to systematically explore branches of possible solutions while “bounding” areas that cannot contain the optimal solution, thereby narrowing down the search efficiently.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {
-      "image/png": {
-       "height": 200,
-       "width": 400
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename='images/branch-and-bound.png',width=400, height=200)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Step-by-Step Process:**\n",
-    "1.\t**Relaxation of Constraints:**\n",
-    "To start, we relax the integer constraints, treating all variables as continuous. This relaxation converts the integer programming problem into a standard linear programming problem, which we can solve efficiently using the **Simplex Method**. The result of this relaxed problem provides a baseline solution that may not necessarily satisfy the integer requirements. For example, if the optimal solution of the relaxed problem gives us a variable  x = 2.6 , we know this is not a valid solution if  x  must be an integer.\n",
-    "\n",
-    "2.\t**Creating Subproblems by Branching:**\n",
-    "When a solution contains a variable that is not an integer (like  x = 2.6 ), we create two subproblems by “branching” on this variable:\n",
-    "    -\t**Branch 1:** Add a constraint that rounds  x  **down** to the nearest integer,  $x \\leq 2$.\n",
-    "    -\t**Branch 2:** Add a constraint that rounds  x  **up** to the next integer,  $x \\geq 3$.\n",
-    "These constraints split the solution space into two smaller, more manageable subproblems. By dividing the problem this way, we explore different regions of the solution space that could lead to an optimal integer solution.\n",
-    "\n",
-    "3.\t**Solving and Bounding:**\n",
-    "We then solve each of these subproblems, again using the Simplex Method, to find the best possible solutions within the new constraints. For each subproblem:\n",
-    "    -\tIf a subproblem yields a solution that is **feasible** (i.e., all variables are integers) and **better** than the current best-known solution, it becomes the new candidate for the optimal solution.\n",
-    "    -\tIf a subproblem’s solution is not feasible (i.e., it still has non-integer variables) or if it cannot improve upon the current best solution, we bound it off. This means we stop exploring that branch further, as it cannot possibly lead to a better solution.\n",
-    "\n",
-    "4.\t**Recursive Exploration:**\n",
-    "The process continues recursively: for each subproblem that still contains non-integer solutions, we branch again, creating further subproblems with tighter constraints. This systematic exploration and bounding help eliminate large swaths of the solution space that cannot contain the optimal solution, making the search much more efficient.\n",
-    "\n",
-    "5.\t**Convergence to the Optimal Solution:**\n",
-    "The Branch and Bound algorithm terminates when all subproblems have been explored or bounded off. At this point, the best feasible solution found is guaranteed to be the **optimal solution** for the original integer programming problem. Unlike heuristic methods, this approach provides a definitive, mathematically-proven optimal solution.\n",
-    "\n",
-    "By combining the efficiency of the Simplex Method for solving linear problems with a systematic search for feasible integer solutions, Branch and Bound is a robust method for tackling complex optimization problems where some or all decision variables must be integers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Stochastic Systems"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "In the context of disaster relief logistics, the environment is not just dynamic but also inherently stochastic. This means that many of the variables involved—such as demand for supplies, transportation times, or the availability of resources—are not deterministic. Instead, they are influenced by a range of unpredictable factors, from weather conditions to sudden changes in infrastructure availability or the emergence of new areas needing assistance. Stochastic systems introduce randomness and uncertainty into optimization problems, requiring specialized approaches that can handle variability effectively.\n",
-    "\n",
-    "1.\tStochastic Optimization: Unlike deterministic optimization methods that assume a fixed environment, stochastic optimization incorporates randomness directly into the problem formulation. Techniques such as Stochastic Gradient Descent (SGD) adapt the optimization process to the presence of noise by using randomly selected subsets of data to update solutions iteratively. This allows the method to converge more quickly on large datasets or complex environments typical of disaster relief operations, where real-time data is continuously being updated.\n",
-    "2.\tModeling Uncertainty with Probabilistic Constraints: Disaster relief problems often require the modeling of uncertainty in the constraints themselves. For instance, a probabilistic constraint might state that the likelihood of delivering sufficient supplies to a location must be at least 95%. Methods like Chance-Constrained Programming and Robust Optimization are designed to handle such constraints, ensuring that solutions are viable even under uncertain conditions.\n",
-    "3.\tMonte Carlo Methods and Simulation-Based Approaches: For highly uncertain systems where analytical solutions are challenging to derive, Monte Carlo simulations provide a practical alternative. These simulations model different possible scenarios by generating random samples from probability distributions associated with uncertain parameters. By running a large number of such simulations, decision-makers can estimate the expected outcomes and identify strategies that are robust across a range of possible futures. This is particularly valuable in disaster relief logistics, where preparing for the worst-case scenarios can save lives.\n",
-    "4.\tReinforcement Learning (RL) for Adaptive Decision Making: RL techniques are increasingly applied to stochastic optimization problems in logistics and supply chain management. In the RL framework, an agent learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties. This approach allows the agent to develop strategies that perform well under uncertainty and adapt to evolving conditions, making it highly suitable for disaster relief scenarios where rapid and flexible response is essential.\n",
-    "5.\tMarkov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs): When dealing with sequential decision-making under uncertainty, MDPs provide a powerful framework to model the problem. In MDPs, decisions are made in stages, and each decision affects both immediate rewards and future states of the system. POMDPs extend this framework to situations where the system’s state is not fully observable, which is often the case in disaster response. For example, the exact condition of a road or the need level in a community may only be partially known due to limited communication. These models help optimize policies that account for both known and unknown elements, providing a balanced approach to decision-making under uncertainty.\n",
-    "6.\tDynamic Programming and Approximate Dynamic Programming: In problems characterized by stochasticity and high dimensionality, exact solutions are often impractical. Dynamic Programming (DP) breaks down the problem into smaller, more manageable subproblems, solving them recursively. However, when the state space is too large (a common issue in real-world logistics), Approximate Dynamic Programming (ADP) or reinforcement learning-based approaches like Q-learning or policy gradients are used to find near-optimal solutions without exhaustive computation.\n",
-    "\n",
-    "In conclusion, handling stochastic systems requires a blend of probabilistic modeling, simulation, and adaptive learning techniques. For disaster relief logistics, where uncertainty is a given and stakes are high, these methods provide the necessary tools to devise strategies that are not only optimal in theory but also robust and flexible in practice. The ability to incorporate randomness, adapt to evolving scenarios, and provide solutions under uncertainty makes stochastic systems analysis a cornerstone of modern optimization approaches in complex, real-world environments."
-   ]
-  }
- ],
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diff --git a/optimization202/ESUPS_case_study/images/Constraint-set-lines-points-number-solutions.jpg.gif b/optimization202/ESUPS_case_study/images/Constraint-set-lines-points-number-solutions.jpg.gif
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diff --git a/optimization202/ESUPS_case_study/images/branch-and-bound.png b/optimization202/ESUPS_case_study/images/branch-and-bound.png
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diff --git a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb
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--- /dev/null
+++ b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb
@@ -0,0 +1,7135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Disaster Pre-positioning with Mathematical Optimization\n",
+    "\n",
+    "In this notebook, we will take you through the end-to-end process of how ESUPS transformed from a simple idea into a global initiative that is reshaping disaster response logistics. We will delve into the crucial role that optimization plays in this transformation, demonstrating how ESUPS utilizes advanced algorithms to optimize the allocation and movement of disaster relief supplies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {
+      "image/png": {
+       "width": 600
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='images/ESUPS-Logo.png', width=600)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. The Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our first step in the journey is reading and cleaning the data. We won't focus too much on this step because the really interesting stuff happens once we have the data loaded. However, we will showcase how the data needs to be formatted for Gurobi. If this is your first pass of the case study, feel free to skip over this section and return to it later when you want a more in-depth overview of formatting the data.\n",
+    "\n",
+    "Before we begin, I wanted to make a quick note about a line of code you'll see repeated throughout the notebook: %%script false --no-raise-error\n",
+    "\n",
+    "\n",
+    "- **NOTE:** This is just cell magic (if you're unfamiliar, think of it as using the cmd line) telling the notebook not to run this cell. We'll use it in various places to demonstrate ideas or code snippets that are not meant to produce an output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 The Environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 4 main libraries we'll be using to solve this problem. The other import statements can be explicitly seen in setup_imports.py, but is primarily for getting all relevant methods from the source repo into our namespace:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gurobipy==11.0.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (11.0.3)\n",
+      "Requirement already satisfied: ipython==8.26.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (8.26.0)\n",
+      "Requirement already satisfied: ipywidgets==8.1.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (8.1.3)\n",
+      "Requirement already satisfied: numpy<2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.26.4)\n",
+      "Requirement already satisfied: pandas==2.2.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 5)) (2.2.2)\n",
+      "Requirement already satisfied: pandera==0.20.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 6)) (0.20.3)\n",
+      "Requirement already satisfied: plotly==5.23.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 7)) (5.23.0)\n",
+      "Requirement already satisfied: pydantic==2.8.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 8)) (2.8.2)\n",
+      "Requirement already satisfied: scipy==1.14.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 9)) (1.14.1)\n",
+      "Requirement already satisfied: tqdm==4.66.4 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 10)) (4.66.4)\n",
+      "Requirement already satisfied: typing_extensions==4.12.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 11)) (4.12.2)\n",
+      "Requirement already satisfied: networkx in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from -r requirements.txt (line 12)) (3.4.2)\n",
+      "Requirement already satisfied: decorator in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipython==8.26.0->-r requirements.txt (line 2)) (5.1.1)\n",
+      "Requirement already satisfied: jedi>=0.16 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipython==8.26.0->-r requirements.txt (line 2)) (0.19.1)\n",
+      "Requirement already satisfied: matplotlib-inline in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipython==8.26.0->-r requirements.txt (line 2)) (0.1.6)\n",
+      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipython==8.26.0->-r requirements.txt (line 2)) (3.0.48)\n",
+      "Requirement already satisfied: pygments>=2.4.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipython==8.26.0->-r requirements.txt (line 2)) (2.16.1)\n",
+      "Requirement already satisfied: stack-data in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipython==8.26.0->-r requirements.txt (line 2)) (0.6.2)\n",
+      "Requirement already satisfied: traitlets>=5.13.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipython==8.26.0->-r requirements.txt (line 2)) (5.14.3)\n",
+      "Requirement already satisfied: pexpect>4.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipython==8.26.0->-r requirements.txt (line 2)) (4.8.0)\n",
+      "Requirement already satisfied: comm>=0.1.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipywidgets==8.1.3->-r requirements.txt (line 3)) (0.1.4)\n",
+      "Requirement already satisfied: widgetsnbextension~=4.0.11 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipywidgets==8.1.3->-r requirements.txt (line 3)) (4.0.13)\n",
+      "Requirement already satisfied: jupyterlab-widgets~=3.0.11 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from ipywidgets==8.1.3->-r requirements.txt (line 3)) (3.0.13)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas==2.2.2->-r requirements.txt (line 5)) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas==2.2.2->-r requirements.txt (line 5)) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas==2.2.2->-r requirements.txt (line 5)) (2023.3)\n",
+      "Requirement already satisfied: multimethod<=1.10.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandera==0.20.3->-r requirements.txt (line 6)) (1.10)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandera==0.20.3->-r requirements.txt (line 6)) (23.2)\n",
+      "Requirement already satisfied: typeguard in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandera==0.20.3->-r requirements.txt (line 6)) (4.4.1)\n",
+      "Requirement already satisfied: typing-inspect>=0.6.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandera==0.20.3->-r requirements.txt (line 6)) (0.9.0)\n",
+      "Requirement already satisfied: wrapt in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandera==0.20.3->-r requirements.txt (line 6)) (1.16.0)\n",
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+      "Requirement already satisfied: annotated-types>=0.4.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pydantic==2.8.2->-r requirements.txt (line 8)) (0.7.0)\n",
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+      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from jedi>=0.16->ipython==8.26.0->-r requirements.txt (line 2)) (0.8.3)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pexpect>4.3->ipython==8.26.0->-r requirements.txt (line 2)) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython==8.26.0->-r requirements.txt (line 2)) (0.2.8)\n",
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+      "Requirement already satisfied: mypy-extensions>=0.3.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from typing-inspect>=0.6.0->pandera==0.20.3->-r requirements.txt (line 6)) (1.0.0)\n",
+      "Requirement already satisfied: executing>=1.2.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from stack-data->ipython==8.26.0->-r requirements.txt (line 2)) (1.2.0)\n",
+      "Requirement already satisfied: asttokens>=2.1.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from stack-data->ipython==8.26.0->-r requirements.txt (line 2)) (2.4.0)\n",
+      "Requirement already satisfied: pure-eval in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from stack-data->ipython==8.26.0->-r requirements.txt (line 2)) (0.2.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -r requirements.txt              \n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math \n",
+    "\n",
+    "import gurobipy as gp\n",
+    "from gurobipy import GRB\n",
+    "\n",
+    "#now hidden code\n",
+    "from setup_imports import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next and final step in this section is to define what data we'll be using to implement the solver, and in this case, we'll be using real-world data from Madagascar. Madagascar is the fourth largest island in the world and is located off the southeastern coast of Africa. Known for its unique biodiversity, approximately 90% of its wildlife is found nowhere else on Earth. The island's diverse ecosystems range from rainforests to deserts, making it a hotspot for biological research and conservation efforts.\n",
+    "\n",
+    "However, Madagascar is also prone to natural disasters, including cyclones, floods, and droughts, all of which have a significant impact on its population and infrastructure. These disasters pose challenges for disaster response and resource allocation, making it an ideal case study for optimization and data-driven decision-making."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set Up Our Data\n",
+    "COUNTRY = \"madagascar\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 Reading and Cleaning \n",
+    "Now we'll load the data into RAM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "warnings.filterwarnings('ignore')\n",
+    "reader = CsvProblemReader()\n",
+    "dataset = reader.read(DATA_DIR / COUNTRY)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While we won't be focusing on reading in the data, it's always useful to see the general structure. Feel free to skip this part on your first high-level pass and come back to it later when you better understand the details.\n",
+    "\n",
+    "You can see in the code below all the factors that go into our model. While intimidating at first glance, this dataset class is a way to tie several related lists and dictionaries to the same name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%script false --no-raise-error\n",
+    "\n",
+    "#The class is as follows\n",
+    "@dataclass(frozen=True)\n",
+    "class Dataset:\n",
+    "    depots: list[Depot]\n",
+    "    disasters: list[Disaster]\n",
+    "    disaster_locations: list[DisasterLocation]\n",
+    "    probabilities: dict[Disaster, float]\n",
+    "    items: list[Item]\n",
+    "    transport_modes: list[TransportMode]\n",
+    "    inventory: dict[Tuple[Depot, Item], int]\n",
+    "    inventory_scenarios: dict[str, dict[Tuple[Depot, Item], int]]\n",
+    "    distance: DistanceMatrix\n",
+    "    people_affected: dict[Tuple[DisasterImpact, Item], float]\n",
+    "    persons_per_item_general: dict[Tuple[DisasterImpact, Item], float]\n",
+    "    persons_per_item_monthly: dict[Tuple[DisasterImpact, Item], float]\n",
+    "    disaster_affected_totals: dict[str, int]\n",
+    "\n",
+    "    _zero_demand_threshold = 1e6\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, the dataset fields are mainly data structures holding other object. Now we won't go into all these here, but it can be useful to see how they are set up. Let's take a look at one such object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%script false --no-raise-error\n",
+    "\n",
+    "@dataclass(frozen=True)\n",
+    "class Item:\n",
+    "    id: str = field(hash=True)\n",
+    "    weight: float = field(repr=False)  # Metric tons\n",
+    "    volume: float = field(repr=False)  # Cubic metres"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, it's a simple data structure, and most of the code here is actually more about organization and readability, which is incredibly useful in production code. Credits here go to Ben Kennerley, who has been working with ESUPS to enhance STOCKHOLM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3 Data Exploration and Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's always useful to see the general structure of data and the broader context of the problem. Now that we've cleaned and loaded it, let's take some time to understand it. The goal here should be to get comfortable with the problem as a whole and give you an intuitive understanding of what we are solving. So, the emphasis isn't on the code. For this reason, most of the following cells have been written as functions to collapse more easily across different platforms. Your goal shouldn't be to understand the libraries used to map, but instead on how Madagascar looks at a high level."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3.1 Where is Everything?\n",
+    "One of the first things we'll look at is where the supplies are relative to disasters right now. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
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+   "source": [
+    "def graph_locations():\n",
+    "    h = 0.1\n",
+    "    l = -0.1\n",
+    "    warehouses = [['warehouses',\n",
+    "                   depot.latitude + np.random.uniform(low=l, high=h),\n",
+    "                   depot.longitude + np.random.uniform(low=l, high=h)] \n",
+    "                   for depot in dataset.depots]\n",
+    "\n",
+    "    disasters = [['disasters',\n",
+    "                  disaster_locations.latitude + np.random.uniform(low=l, high=h),\n",
+    "                  disaster_locations.longitude + np.random.uniform(low=l, high=h)] \n",
+    "                  for disaster_locations in dataset.disaster_locations]\n",
+    "\n",
+    "    dfw = pd.DataFrame(warehouses)\n",
+    "    dfw.columns = ['Type', 'Lat', 'Long']\n",
+    "\n",
+    "    dfd = pd.DataFrame(disasters)\n",
+    "    dfd.columns = ['Type', 'Lat', 'Long']\n",
+    "\n",
+    "    df_combined = pd.concat([dfw[['Lat', 'Long', 'Type']], dfd[['Lat', 'Long', 'Type']]], ignore_index=True)\n",
+    "\n",
+    "    fig = px.scatter_mapbox(df_combined, \n",
+    "                            lat=\"Lat\", \n",
+    "                            lon=\"Long\", \n",
+    "                            color=\"Type\",\n",
+    "                            color_discrete_sequence=px.colors.qualitative.Safe,\n",
+    "                            zoom=4.5)\n",
+    "    \n",
+    "    # Use a minimalist map style to reduce clutter\n",
+    "    fig.update_layout(mapbox_style=\"open-street-map\")\n",
+    "\n",
+    "    # Optionally center the map around the mean latitude and longitude of your points\n",
+    "    mean_lat = df_combined['Lat'].mean()\n",
+    "    mean_long = df_combined['Long'].mean()\n",
+    "    fig.update_layout(mapbox_center={\"lat\": mean_lat, \"lon\": mean_long})\n",
+    "    fig.update_layout(margin={\"r\": 0, \"t\": 0, \"l\": 0, \"b\": 0})\n",
+    "\n",
+    "    fig.show()\n",
+    "\n",
+    "graph_locations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the warehouses are aligned closely with the most prominent disaster sights*, so assuming they are built to a level that can survive and protect against those disasters, Madagascar should be in a strong position in terms of coverage. However, there's more than meets the eye. Let's dive a little deeper!\n",
+    "\n",
+    "Hopefully, now that you've gotten a sense of the country and the layout, we'll turn off some of the more detailed parts (such as roads and urbanization) so it's simpler to see what’s going on.\n",
+    "\n",
+    "- **NOTE:** locations of warehouses have been modified for this case study for safety."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3.2 What Supplies are Available?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An important question to ask at this point is: what do we have inside the warehouses? It's great that there seems to be good coverage that ensures a warehouse is nearby for any disaster, but if one of them is full of kitchen sets and there's a flood, they might not be as immediately useful as buckets or tarpaulins. So, let's look at the overall breakdown of supplies by warehouse/location:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
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+            "ticks": "",
+            "zerolinecolor": "white"
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+           "line": {
+            "color": "#2a3f5f"
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+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
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+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Distribution of Supplies for Disaster Relief",
+         "x": 0.5,
+         "xanchor": "center",
+         "y": 0.95,
+         "yanchor": "top"
+        },
+        "xaxis": {
+         "tickangle": -45,
+         "tickfont": {
+          "size": 10
+         },
+         "title": {
+          "text": "Supplies"
+         }
+        },
+        "yaxis": {
+         "tickfont": {
+          "size": 10
+         },
+         "title": {
+          "text": "Quantity"
+         },
+         "type": "log"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "# Sample Data\n",
+    "def make_barchart():\n",
+    "    items = {}\n",
+    "    for x in dataset.inventory:\n",
+    "        if x[1].id in items:\n",
+    "            items[x[1].id] += dataset.inventory[x]\n",
+    "        else:\n",
+    "            items[x[1].id] = dataset.inventory[x]\n",
+    "\n",
+    "    items = dict(sorted(items.items(), key=lambda item: item[1], reverse=True))\n",
+    "\n",
+    "    # Extract keys and values from the dictionary\n",
+    "    categories = list(items.keys())\n",
+    "    values = list(items.values())\n",
+    "\n",
+    "    # Normalize the values for color scale\n",
+    "    normalized_values = np.array(values) / max(values)\n",
+    "\n",
+    "    # Define a color scale (e.g., 'Viridis', 'Cividis', 'Plasma', etc.)\n",
+    "    colorscale = 'Cividis'\n",
+    "\n",
+    "    def format_number(num):\n",
+    "        if num >= 1_000_000:\n",
+    "            return f'{num/1_000_000:.1f}M'  # Format as millions\n",
+    "        elif num >= 1_000:\n",
+    "            return f'{num/1_000:.0f}k'  # Format as thousands\n",
+    "        else:\n",
+    "            return str(num)  # Show the number as is if below 1,000\n",
+    "\n",
+    "    # Create formatted text for display\n",
+    "    formatted_text = [format_number(v) for v in values]  # Use the custom formatting function\n",
+    "\n",
+    "    # Create a bar chart with a color continuum\n",
+    "    fig = go.Figure(\n",
+    "        data=[\n",
+    "            go.Bar(\n",
+    "                x=categories,\n",
+    "                y=values,\n",
+    "                marker=dict(\n",
+    "                    color=normalized_values,  # Use normalized values for the color scale\n",
+    "                    colorscale=colorscale,  # Apply the chosen color scale\n",
+    "                    showscale=False  # Hide the color scale bar\n",
+    "                    \n",
+    "                ),\n",
+    "                texttemplate='%{text}',  # Explicitly set text template\n",
+    "                text=formatted_text,  # Display formatted text with 'k' and 'M' suffixes\n",
+    "                hovertemplate='<b>%{x}</b><br>Quantity: %{y}<extra></extra>',  # Show exact hover info\n",
+    "                textposition='auto',  # Automatically position the text\n",
+    "            )\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    # Update layout\n",
+    "    fig.update_layout(\n",
+    "        title={\n",
+    "            'text': \"Distribution of Supplies for Disaster Relief\",\n",
+    "            'y': 0.95,\n",
+    "            'x': 0.5,\n",
+    "            'xanchor': 'center',\n",
+    "            'yanchor': 'top'\n",
+    "        },\n",
+    "        xaxis_title=\"Supplies\",\n",
+    "        yaxis_title=\"Quantity\",\n",
+    "        yaxis_type=\"log\",  # Logarithmic scale for better visualization\n",
+    "        xaxis=dict(tickangle=-45),  # Rotate x-axis labels for better readability\n",
+    "        plot_bgcolor='white',  # Set background color to white for better contrast\n",
+    "        xaxis_tickfont_size=10,  # Font size for x-axis labels\n",
+    "        yaxis_tickfont_size=10,  # Font size for y-axis labels\n",
+    "        margin=dict(l=40, r=40, t=80, b=100),  # Adjust margins\n",
+    "    )\n",
+    "\n",
+    "    # Add annotations to highlight key insights\n",
+    "    fig.add_annotation(\n",
+    "        x=categories[0],  # Example: Highlighting the highest value category\n",
+    "        y=values[0],\n",
+    "        text=\"Highest Quantity\",\n",
+    "        showarrow=True,\n",
+    "        arrowhead=2,\n",
+    "        ax=-40,\n",
+    "        ay=-40\n",
+    "    )\n",
+    "\n",
+    "    # Show the plot\n",
+    "    fig.show()\n",
+    "\n",
+    "make_barchart()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see from above, the supplies skew heavily towards buckets, water containers, and mosquito nets, which makes sense for an island nation. But while it's great that we have a lot of buckets, it won't do us too much good if none of them are at the coast, for instance.\n",
+    "\n",
+    "- **NOTE:** From here out in the case study, we're going to focus on just buckets as they're the most prevalent item and, for the purposes of this case, it's time consuming and repetitive to analyze all 15 item types of supplies available. \n",
+    "\n",
+    "As you'll see later in the notebook, it's a simple matter to apply the analysis of any one of the supply items to all 15 of them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3.4 Where are the Supplies?\n",
+    "\n",
+    "Let's take a look at the following map to get a better picture of where everything is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "Buckets=%{marker.size}<br>Lat=%{lat}<br>Long=%{lon}<extra></extra>",
+         "lat": [
+          -13.6804,
+          -17.8237,
+          -20.5167,
+          -25.17613271,
+          -18.9085,
+          -14.8762,
+          -22.8167,
+          -17.3843,
+          -15.7167,
+          -18.0646,
+          -22.15,
+          -18.9608,
+          -20.2847,
+          -14.2667,
+          -18.1499,
+          -23.35
+         ],
+         "legendgroup": "",
+         "lon": [
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+          48.4263,
+          47.25,
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+          47.5375,
+          47.9835,
+          47.8167,
+          49.4098,
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+          48,
+          46.9036,
+          44.3175,
+          50.1667,
+          49.4023,
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+         "showlegend": false,
+         "subplot": "mapbox",
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+       "layout": {
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+   ],
+   "source": [
+    "def graph_supplies():\n",
+    "  supplies=[[x[0].latitude,\n",
+    "             x[0].longitude,\n",
+    "             dataset.inventory[x]]\n",
+    "             for x in dataset.inventory if x[1].id=='Buckets']\n",
+    " \n",
+    "  df=pd.DataFrame(supplies)\n",
+    "  df.columns = ['Lat', 'Long', 'Buckets']\n",
+    "\n",
+    "  fig = px.scatter_mapbox(df, \n",
+    "                        lat=\"Lat\", \n",
+    "                        lon=\"Long\", \n",
+    "                        size=\"Buckets\",\n",
+    "                        #size_max=10,  # Maximum size of the marker\n",
+    "                        #size_min=5,\n",
+    "                        color_discrete_sequence=px.colors.qualitative.Safe,\n",
+    "                        zoom=4.5,  # Adjust zoom level as needed\n",
+    "                        )\n",
+    "\n",
+    "  # Optionally center the map around the mean latitude and longitude of your points\n",
+    "  # Use a minimalist map style to reduce clutter\n",
+    "  fig.update_layout(mapbox_style=\"carto-positron\")\n",
+    "\n",
+    "  mean_lat = df['Lat'].mean()\n",
+    "  mean_long = df['Long'].mean()\n",
+    "  fig.update_layout(mapbox_center={\"lat\": mean_lat, \"lon\": mean_long})\n",
+    "  fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
+    "\n",
+    "  fig.show()\n",
+    "graph_supplies()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the supplies are largely located on the eastern coast line of the country"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3.5 Not All Disasters Are Built the Same"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
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+   ],
+   "source": [
+    "def graph_impacts():\n",
+    "    \n",
+    "    # Example values for l and h\n",
+    "    l = -0.1\n",
+    "    h = 0.1\n",
+    "\n",
+    "    # Sample data preparation\n",
+    "    disasters = [[disasters.type.id,\n",
+    "                disaster_locations.latitude,\n",
+    "                disaster_locations.longitude,\n",
+    "                dataset.disaster_affected_totals[key]] \n",
+    "                for disasters, disaster_locations, key in zip(dataset.disasters, dataset.disaster_locations, dataset.disaster_affected_totals)]\n",
+    "\n",
+    "    df = pd.DataFrame(disasters)\n",
+    "    df.columns = ['Type', 'Lat', 'Long', 'People Impacted']\n",
+    "\n",
+    "    fig = px.scatter_mapbox(df, \n",
+    "                            lat=\"Lat\", \n",
+    "                            lon=\"Long\", \n",
+    "                            color=\"Type\", \n",
+    "                            size=\"People Impacted\",\n",
+    "                            #size_max=10,  # Maximum size of the marker\n",
+    "                            #size_min=5,\n",
+    "                            zoom=4.5,  # Adjust zoom level as needed\n",
+    "                            color_discrete_sequence=px.colors.qualitative.Safe,\n",
+    "                            )\n",
+    "\n",
+    "    # Optionally center the map around the mean latitude and longitude of your points\n",
+    "    # Use a minimalist map style to reduce clutter\n",
+    "    fig.update_layout(mapbox_style=\"carto-positron\")\n",
+    "\n",
+    "    mean_lat = df['Lat'].mean()\n",
+    "    mean_long = df['Long'].mean()\n",
+    "    fig.update_layout(mapbox_center={\"lat\": mean_lat, \"lon\": mean_long})\n",
+    "    fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
+    "\n",
+    "    fig.show()\n",
+    "\n",
+    "graph_impacts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1.3.6 How Do Disasters and Supplies Compare in Scale?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see small blue circles within the red circles, representing supply quantities. The size of each red circle indicates the estimated number of people needing supplies in a disaster-affected area. Meanwhile, the blue circles show the total number of items available, adjusted by how many people each item can serve. For example, one large bucket is estimated to meet the needs of 2.5 people, so the blue circles display the quantity of supplies as $2.5 \\cdot Supplies$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "Type=supplies<br>Scale=%{marker.size}<br>Lat=%{lat}<br>Long=%{lon}<extra></extra>",
+         "lat": [
+          -13.6804,
+          -17.8237,
+          -20.5167,
+          -25.17613271,
+          -18.9085,
+          -14.8762,
+          -22.8167,
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+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def graph_overlap():\n",
+    "  supplies=[['supplies',\n",
+    "             x[0].latitude,\n",
+    "             x[0].longitude,\n",
+    "             dataset.inventory[x]*2.5] #this is people per bucket\n",
+    "             for x in dataset.inventory if x[1].id=='Buckets']\n",
+    " \n",
+    "  supplies=pd.DataFrame(supplies)\n",
+    "  supplies.columns = ['Type','Lat', 'Long', 'Scale']\n",
+    "\n",
+    "\n",
+    "  # Sample data preparation\n",
+    "  disasters = [['disasters',\n",
+    "                disaster_locations.latitude,\n",
+    "                disaster_locations.longitude,\n",
+    "                dataset.disaster_affected_totals[key]] \n",
+    "                for disasters, disaster_locations, key in zip(dataset.disasters, dataset.disaster_locations, dataset.disaster_affected_totals)]\n",
+    "\n",
+    "  disasters = pd.DataFrame(disasters)\n",
+    "  \n",
+    "  \n",
+    "  disasters.columns = ['Type', 'Lat', 'Long', 'Scale']\n",
+    "\n",
+    "  df_combined = pd.concat([supplies[['Type','Lat', 'Long', 'Scale']],\n",
+    "                           disasters[['Type','Lat', 'Long', 'Scale']]],\n",
+    "                           ignore_index=True)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "  fig = px.scatter_mapbox(df_combined, \n",
+    "                        lat=\"Lat\", \n",
+    "                        lon=\"Long\", \n",
+    "                        size=\"Scale\",\n",
+    "                        color='Type',\n",
+    "                        size_max=40,  # Maximum size of the marker\n",
+    "                        #size_min=5,\n",
+    "                        zoom=4.5,  # Adjust zoom level as needed\n",
+    "                        color_discrete_sequence=px.colors.qualitative.Safe,\n",
+    "                        )\n",
+    "\n",
+    "  # Optionally center the map around the mean latitude and longitude of your points\n",
+    "  # Use a minimalist map style to reduce clutter\n",
+    "  fig.update_layout(mapbox_style=\"carto-positron\")\n",
+    "\n",
+    "  mean_lat = df_combined['Lat'].mean()\n",
+    "  mean_long = df_combined['Long'].mean()\n",
+    "  fig.update_layout(mapbox_center={\"lat\": mean_lat, \"lon\": mean_long})\n",
+    "  fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
+    "\n",
+    "  fig.show()\n",
+    "\n",
+    "graph_overlap()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.4 How Does ESUPS Communicate this?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ESUPS's platform  [Stockholm](https://www.esups-stockholm.org/#/private/signin), which is their platform for hosting this model (you can apply for access by visiting the homepage linked above), has two main pages, the first is the context page, and explains the context of the problem that we've been discussing in the past few sections, along with additional details about their mission and collaborating organizations. We'll look at this one first!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**The Real-time Data Visualization** : Progressive Improvement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're going to take a slight detour here to look at how all this information is communicated in real life! You can skip this section without concern for missing important information. \n",
+    "\n",
+    "As we've seen, getting an intuitive understanding of disasters and humanitarian supplies can be tricky when working with a new country. In fact, it's one of the main challenges ESUPS has faced in driving adoption and explaining why their work is so important. \n",
+    "\n",
+    "Data Visualization isn't an easy task, but let's take a look at how ESUPS started and how they're continuing to improve!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {
+      "image/png": {
+       "width": 1200
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='images/Old_Stockholm.png', width=1200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The earlier version of the ESUPS dashboard, while not perfect, played a critical role in enabling the organization to quickly deliver a functional product that demonstrated tangible results to other non-profits. This initial version provided the essential tools needed to map stockpiles and showcase the value of the platform in real-world scenarios. By prioritizing functionality and speed, ESUPS was able to meet the immediate needs of its partners, proving the concept and gaining crucial buy-in from stakeholders.\n",
+    "\n",
+    "The design, though basic, allowed for rapid deployment and iteration, proving that in the early stages of development, it's more important to have a working solution than to strive for perfection. The dashboard's ability to deliver key insights swiftly and effectively earned it significant merit, as it laid the groundwork for future improvements and set the stage for more advanced iterations. This approach underscores a vital lesson in development: sometimes, a good solution delivered quickly can be more valuable than a perfect one delivered too late.\n",
+    "\n",
+    "The initial success provided a strong foundation upon which ESUPS could build. The insights gained from this deployment informed the development of a new version, which addresses the limitations of the first while enhancing user experience and data communication. The new dashboard takes these lessons to heart, incorporating more advanced features and a more intuitive design, ensuring that ESUPS continues to meet the needs of its partners more effectively. Now, let’s explore how this new version improves upon the original and further advances ESUPS's mission."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {
+      "image/png": {
+       "width": 1200
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename='images/Disaster_Dashboard.png',width=1200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Optimizing!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 Intro to the ESUPS Optimization Model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let:\n",
+    "\n",
+    "- $\\tau_{ij}$ be the time to ship a single item from warehouse $i$ to the disaster\n",
+    "\n",
+    "- $y_i$ be the amount of supplies to send from warehouse $i$ to our disaster\n",
+    "\n",
+    "- $x_i$ be the starting inventory at each warehouse\n",
+    "\n",
+    "- $d$ be the demand for an item\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have described what we can change with our variables, we can figure out how to represent the objective function!\n",
+    "$$\n",
+    "\\min_y \\sum_i \\tau_{i}\\cdot y_i\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The constraints are:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\text{s.t.}  & \\sum_{i} & y_{i}&=d & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n",
+    "\n",
+    "& & y_i       &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
+    "\n",
+    " &\\text{} & y_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\n",
+    "\\end{aligned}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-warning\">\n",
+    "    <strong>Note!</strong>\n",
+    "    <p>Decision variables in mathematical optimization problems are typically assumed to be nonnegative. So while you'll see these constraints in formulations (ei.e. the algebraic representations), you may not see the code for it since it's likely assumed to be nonnegative.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So now let's solve this model. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Create The Model Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Restricted license - for non-production use only - expires 2025-11-24\n"
+     ]
+    }
+   ],
+   "source": [
+    "#prep distance matrix\n",
+    "df_distance, relevant_warehouses, BucketsNeeded = get_distance_matrix(dataset)\n",
+    "t = df_distance.drivingTime_hrs\n",
+    "n = len(relevant_warehouses)\n",
+    "\n",
+    "# Create model\n",
+    "model = gp.Model(\"simple_Allocation\") \n",
+    "\n",
+    "# Add decision variables\n",
+    "y=model.addVars(n, vtype=GRB.INTEGER, name=\"Warehouse_Allocation\")\n",
+    "\n",
+    "#Add constraint to meet demand\n",
+    "model.addConstr(gp.quicksum(y[i] for i in range(n))==BucketsNeeded,name='Meet_Demand')\n",
+    "\n",
+    "# Add in warehouse_constraints\n",
+    "for i, supplies in enumerate(relevant_warehouses):\n",
+    "    model.addConstr(y[i] <= supplies[2], name=f\"warehouse_endowment_{i}\")\n",
+    "\n",
+    "# Note we don't have a constraint for y >= 0 since it's assumed in the variable definition\n",
+    "# Add objective\n",
+    "objective = gp.quicksum(t[i] * y[i] for i in range(n))\n",
+    "model.setObjective(objective, GRB.MINIMIZE)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
+      "\n",
+      "CPU model: Apple M1\n",
+      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
+      "\n",
+      "Optimize a model with 17 rows, 16 columns and 32 nonzeros\n",
+      "Model fingerprint: 0x0ab092cf\n",
+      "Variable types: 0 continuous, 16 integer (0 binary)\n",
+      "Coefficient statistics:\n",
+      "  Matrix range     [1e+00, 1e+00]\n",
+      "  Objective range  [6e+00, 3e+01]\n",
+      "  Bounds range     [0e+00, 0e+00]\n",
+      "  RHS range        [3e+00, 1e+04]\n",
+      "Found heuristic solution: objective 186156.00000\n",
+      "Presolve removed 16 rows and 2 columns\n",
+      "Presolve time: 0.00s\n",
+      "Presolved: 1 rows, 14 columns, 14 nonzeros\n",
+      "Variable types: 0 continuous, 14 integer (0 binary)\n",
+      "Found heuristic solution: objective 186086.75000\n",
+      "\n",
+      "Root relaxation: objective 9.829300e+04, 1 iterations, 0.00 seconds (0.00 work units)\n",
+      "\n",
+      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
+      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
+      "\n",
+      "*    0     0               0    98293.000000 98293.0000  0.00%     -    0s\n",
+      "\n",
+      "Explored 1 nodes (1 simplex iterations) in 0.03 seconds (0.00 work units)\n",
+      "Thread count was 8 (of 8 available processors)\n",
+      "\n",
+      "Solution count 3: 98293 186087 186156 \n",
+      "\n",
+      "Optimal solution found (tolerance 1.00e-04)\n",
+      "Best objective 9.829300000000e+04, best bound 9.829300000000e+04, gap 0.0000%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fire up the solver!\n",
+    "model.optimize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now Let's Analyze the results!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Var</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>possible</th>\n",
+       "      <th>distance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Warehouse_Allocation[1]</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Warehouse_Allocation[4]</td>\n",
+       "      <td>9046.0</td>\n",
+       "      <td>9046</td>\n",
+       "      <td>6.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Warehouse_Allocation[11]</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Warehouse_Allocation[14]</td>\n",
+       "      <td>1580.0</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>8.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Warehouse_Allocation[5]</td>\n",
+       "      <td>610.0</td>\n",
+       "      <td>610</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Warehouse_Allocation[2]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Warehouse_Allocation[7]</td>\n",
+       "      <td>2296.0</td>\n",
+       "      <td>6689</td>\n",
+       "      <td>11.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Warehouse_Allocation[0]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>375</td>\n",
+       "      <td>14.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Warehouse_Allocation[9]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>2460</td>\n",
+       "      <td>14.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Warehouse_Allocation[8]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>150</td>\n",
+       "      <td>16.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Warehouse_Allocation[12]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>736</td>\n",
+       "      <td>16.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Warehouse_Allocation[10]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>4235</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Warehouse_Allocation[6]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>5201</td>\n",
+       "      <td>19.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Warehouse_Allocation[15]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>1637</td>\n",
+       "      <td>22.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Warehouse_Allocation[13]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>5700</td>\n",
+       "      <td>23.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Warehouse_Allocation[3]</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>2322</td>\n",
+       "      <td>26.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Var  amount  possible  distance\n",
+       "1    Warehouse_Allocation[1]    26.0        26      0.00\n",
+       "4    Warehouse_Allocation[4]  9046.0      9046      6.00\n",
+       "11  Warehouse_Allocation[11]     3.0         3      7.00\n",
+       "14  Warehouse_Allocation[14]  1580.0      1580      8.00\n",
+       "5    Warehouse_Allocation[5]   610.0       610     10.00\n",
+       "2    Warehouse_Allocation[2]    -0.0        41     11.00\n",
+       "7    Warehouse_Allocation[7]  2296.0      6689     11.00\n",
+       "0    Warehouse_Allocation[0]    -0.0       375     14.00\n",
+       "9    Warehouse_Allocation[9]    -0.0      2460     14.25\n",
+       "8    Warehouse_Allocation[8]     0.0       150     16.00\n",
+       "12  Warehouse_Allocation[12]    -0.0       736     16.00\n",
+       "10  Warehouse_Allocation[10]    -0.0      4235     17.00\n",
+       "6    Warehouse_Allocation[6]    -0.0      5201     19.00\n",
+       "15  Warehouse_Allocation[15]    -0.0      1637     22.00\n",
+       "13  Warehouse_Allocation[13]    -0.0      5700     23.00\n",
+       "3    Warehouse_Allocation[3]    -0.0      2322     26.00"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Show Solution\n",
+    "b=[]\n",
+    "for v, total, dis in zip(model.getVars(),relevant_warehouses,list(df_distance['drivingTime_hrs'])):\n",
+    "    if v.VarName[0:20]== 'Warehouse_Allocation':\n",
+    "        #print('%s %g | total= %g | distance=%g' % (v.VarName, v.X,total[2],dis))\n",
+    "        b.append([v.VarName, v.X,total[2],dis])\n",
+    "b=pd.DataFrame(b)\n",
+    "b.columns=['Var','amount','possible','distance' ]\n",
+    "b.sort_values('distance')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you may have noticed that this feels like overkill. If we want to position supplies to respond to a known disaster, you might think that we should just put them as close as possible. It's an intuitive solution that can be solved with a simple greedy algorithm. But of course, life is never that simple. \n",
+    "\n",
+    "Now that we've got the initial problem outlined, let's start making it more realistic with two additions:\n",
+    "1.\tInstead of preparing for only one disaster, let's prepare for all the disasters that might occur.\n",
+    "2.\tInstead of being an omniscient observer, let's say we aren't sure where the next disaster will be"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Including All Disasters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we begin, let's explicitly define our new problem with the additional requirements outlined in the previous section so we're all on the same page. Our first step is to add in the fact that there are more disasters than just one. We can do that by including a variable to denote which disaster we're talking about\n",
+    "\n",
+    "Let:\n",
+    "\n",
+    "- $k$ the disaster scenario at hand i.e. a storm, earthquake, or epidemic\n",
+    "- $j$ be the location of the disaster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In scenario $k$ with the disaster located at $j$, the time for a warehouse $i$  to send $y_i$ items is "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " $$\\tau_{ij}\\cdot y^k_i$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeating for all warehouses gives us"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$  \\sum_i \\tau_{ij}\\cdot y^k_i $$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Repeating for all disasters gives us"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$ \\sum_k \\sum_i \\tau_{ij}\\cdot y^k_i $$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This formulation doesn't change any of the solutions we found in the last section, instead, it's leveraging the power of our notation to be able to solve for the optimal allocation for every disaster in one fell swoop! It's been a while since we looked at the original problem in its entirety, so let's take a step back to understand why this is so important."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we get to solving, let's add in one more small factor. Different disasters occur at different rates. A landlocked nation may be less likely to experience a disaster caused by a tropical storm than an earthquake, so shouldn't we weigh the response time to earthquakes more than that of a storm?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Accounting for Randomness"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most difficult tasks that we can encounter in data science problems is randomness or as it's often called \"stochastic\" elements. This can be seen in all kinds of ways, but in our case study, we're going to look at how we account for not knowing which disaster will hit next or even several years in the future. The way we do this is to create a stochastic model, which may initially seem intimidating, but in discrete events like this, it's super easy.\n",
+    "\n",
+    "If you're familiar with expected value this will quickly make sense, but no need to have any prior experience! The idea is that we weigh the outcome of the event (i.e. total travel time in this case) by the probability it occurs. So, if an earthquake is 3 times as likely as a flood, we would rewrite the equation as:\n",
+    "\n",
+    "$$.75 \\cdot\\text{(travel time earthquake)} + .25 \\cdot\\text{(travel time flood)} $$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.3.1 More On Expected Value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you haven't seen expected value before or if it's just a been a while and you'd like a refresher, try some of the practice problems below!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we solve expected value for discrete outcomes, we take the value/outcome/payoff of each possible event and discount it by the probability that it actually occurs. So it follows the form\n",
+    "\n",
+    "$$\n",
+    "E[X]=p_1(x_1)+p_2(x_2)+ ... + p_n(x_n)\n",
+    "$$\n",
+    "To simplify this notation we typically write this as a series:\n",
+    "\n",
+    "$$\n",
+    "E[X]=\\sum_{i=1}^{i=n} p_i \\cdot x_i\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Starter Problems**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Coin Flip: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppose we play a coin flipping game. Every time we flip the coin and it lands on heads, you get a dollar, and every time it lands on tails, you have to pay a dollar. How much are you expected to make or lose when playing this game?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m 1) What is the Objective Function? \u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5fdae22271cf48789dc0fd35180320c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "RadioButtons(options=(('-1', 1), ('0', 2), ('1', 3)), value=1)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa0898d5db0a4eb181b6acb3c3a78825",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Button(description='Check', style=ButtonStyle())"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "334b1d4f419344a28bb421d64457b720",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q_3_1()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Coin Flip Variant: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's change the game a little, say you now get 5 paid dollars when the coin lands on heads and only have to pay 3 dollars when it lands on tails. Would you play? How much are you expected to make or lose when playing this game?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m 1) What is the Objective Function? \u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fad507aa29242c189e56dfa436d6599",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "RadioButtons(options=(('0', 1), ('1/2', 2), ('1', 3)), value=1)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e40781cb87e476f8264dce0878a42eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Button(description='Check', style=ButtonStyle())"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f10f550a65b4565b18ea8c58f07d3c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q_3_2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Simple Dice Game:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Suppose you roll a fair six-sided die. If it lands on 6, you win $10; otherwise, you lose $2. What is the expected value of this game? Should you play it?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "E[X] &= \\left(\\frac{1}{6} \\times 10\\right) + \\left(\\frac{5}{6} \\times (-2)\\right)\n",
+    "\\\\\n",
+    "E[X] &= \\left(\\frac{10}{6}\\right) + \\left(\\frac{-10}{6}\\right)\n",
+    "\\\\\n",
+    "E[X] &= \\frac{10 - 10}{6} = 0\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "**Answer**: The expected value is $\\$0$. This means, on average, you neither gain nor lose money from playing this game.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Two-Coin Game:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "You flip two fair coins. If both coins land on heads, you win $8. If one coin lands on heads and the other on tails, you win $4. If both coins land on tails, you lose $5. What is the expected value of playing this game?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Solution:\n",
+    "\n",
+    "\n",
+    "- Probability of two heads (HH): $\\frac{1}{4}$\n",
+    "- Probability of one head and one tail (HT or TH): $\\frac{1}{2}$\n",
+    "- Probability of two tails (TT): $\\frac{1}{4}$\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "E[X] &= \\left(\\frac{1}{4} \\times 8\\right) + \\left(\\frac{1}{2} \\times 4\\right) + \\left(\\frac{1}{4} \\times (-5)\\right)\n",
+    "\\\\\n",
+    "E[X] &= \\left(2\\right) + \\left(2\\right) + \\left(-1.25\\right)\n",
+    "\\\\\n",
+    "E[X] &= 2 + 2 - 1.25 = 2.75\n",
+    "\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "**Answer**: The expected value is $\\$2.75$. This means, on average, you gain $\\$2.75$ per game.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Intermediate Problems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Custom Die Game:**\n",
+    "Imagine a custom die with faces labeled 1, 2, 3, 4, 5, and 6. Each face has a different probability: \\(P(1) = 0.1\\), \\(P(2) = 0.2\\), \\(P(3) = 0.15\\), \\(P(4) = 0.25\\), \\(P(5) = 0.2\\), and \\(P(6) = 0.1\\). If the outcome of the die is 1, you win $2; for a 2, you win $4; for a 3, you win $6; for a 4, you lose $3; for a 5, you lose $7; and for a 6, you lose $5. What is the expected value of this game?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Solution:\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "E[X] &= (0.1 \\times 2) + (0.2 \\times 4) + (0.15 \\times 6) + (0.25 \\times -3) + (0.2 \\times -7) + (0.1 \\times -5)\n",
+    "\n",
+    "\\\\\n",
+    "E[X] &= (0.2) + (0.8) + (0.9) + (-0.75) + (-1.4) + (-0.5)\n",
+    "\\\\\n",
+    "\n",
+    "E[X] &= 1.9 - 2.65 = -0.75\n",
+    "\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "**Answer**:The expected value is $-\\$0.75$. On average, you lose $75$ cents per roll.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Card Drawing Game:**\n",
+    "You draw a card from a standard deck of 52 playing cards. If you draw an Ace, you win $15. If you draw a King, Queen, or Jack, you win $5. For any other card, you lose $3. What is the expected value of this game?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Solution:\n",
+    "\n",
+    "- Probability of drawing an Ace: $\\frac{4}{52} = \\frac{1}{13}$\n",
+    "- Probability of drawing a King, Queen, or Jack: $\\frac{12}{52} = \\frac{3}{13}$\n",
+    "- Probability of drawing any other card: $\\frac{36}{52} = \\frac{9}{13}$\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\n",
+    "E[X] &= \\left(\\frac{1}{13} \\times 15\\right) + \\left(\\frac{3}{13} \\times 5\\right) + \\left(\\frac{9}{13} \\times (-3)\\right)\n",
+    "\\\\\n",
+    "\n",
+    "\n",
+    "E[X] &= \\left(\\frac{15}{13}\\right) + \\left(\\frac{15}{13}\\right) + \\left(\\frac{-27}{13}\\right)\n",
+    "\\\\\n",
+    "\n",
+    "\n",
+    "E[X] &= \\frac{15 + 15 - 27}{13} = \\frac{3}{13}\n",
+    "\n",
+    "\\\\\n",
+    "E[X] &\\approx 0.23\n",
+    "\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "**Answer**: The expected value is approximately \\$0.23. On average, you gain 23 cents per draw.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "##### Advanced Extension Problems \n",
+    "\n",
+    "5. **Roulette Game:**\n",
+    "   You bet $1 on a single number in a game of American Roulette. If the ball lands on your number, you win $35; otherwise, you lose your bet. There are 38 slots on an American Roulette wheel (numbers 1-36, 0, and 00). What is the expected value of this bet?\n",
+    "\n",
+    "6. **Insurance Policy:**\n",
+    "   An insurance company sells a one-year term life insurance policy for $500 to a healthy 30-year-old. The policy pays $100,000 in case of death within the year. The probability of a healthy 30-year-old dying within a year is 0.001. What is the expected profit for the insurance company from selling this policy?\n",
+    "\n",
+    "7. **Stock Investment Scenario:**\n",
+    "   Suppose you invest in a stock with three possible outcomes over a year: there is a 50% chance it will increase by 10%, a 30% chance it will decrease by 5%, and a 20% chance it will decrease by 15%. What is the expected value of the return on investment after one year?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.3.2 The Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let $P^k$ be the probability of disaster $k$ and $t^k$ be the total travel time involved in disaster $k$ in the previous sections. Using our definition of expected value, this gives us:\n",
+    "\n",
+    "$$ \\sum_k P^k \\cdot t^k$$\n",
+    "\n",
+    "Now you might notice that we have already written an equation for the total travel time for disaster $k$ ! Substituting this in we get:\n",
+    "\n",
+    "$$\\sum_k P^k \\sum_i \\tau_{ij}\\cdot y^k_i$$\n",
+    "\n",
+    "So, our final task right now is to minimize the time required to get supplies to a disaster given how likely each disaster is to occur:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "$$\n",
+    "\\min_y \\sum_k P^k \\sum_i \\tau_{ij}\\cdot y^k_i\n",
+    "\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can see the constraints are almost unchanged. The only added part is that all probabilities must sum to one, which is always the case:\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\n",
+    "\n",
+    "\\text{s.t.}  & \\sum_{i} & y^k_{i}&=d^k & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n",
+    "\n",
+    "& & y^k_i       &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
+    "\n",
+    " &\\text{} & y^k_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n",
+    "\n",
+    "\n",
+    "\\end{aligned}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How do we calculate the probability though? Well, we have good long-term data on what disasters have affected which countries. For now, we can go through that data and calculate the probability of disaster k by counting how many times it has occurred and dividing by the number of total disasters i.e.\n",
+    "\n",
+    "$$ P^k = \\frac{k}{\\|K\\|}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So now that we have the equation, we can let the model go ahead and let Gurobi solve it for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
+      "\n",
+      "CPU model: Apple M1\n",
+      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "GurobiError",
+     "evalue": "Model too large for size-limited license; visit https://gurobi.com/unrestricted for more information",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mGurobiError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[28], line 31\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Optimize model\u001b[39;00m\n\u001b[1;32m     30\u001b[0m model\u001b[38;5;241m.\u001b[39msetObjective(objective, GRB\u001b[38;5;241m.\u001b[39mMINIMIZE)\n\u001b[0;32m---> 31\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;66;03m# Store results in the list 'a'\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mgetVars():\n",
+      "File \u001b[0;32msrc/gurobipy/model.pxi:893\u001b[0m, in \u001b[0;36mgurobipy.Model.optimize\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mGurobiError\u001b[0m: Model too large for size-limited license; visit https://gurobi.com/unrestricted for more information"
+     ]
+    }
+   ],
+   "source": [
+    "demand, probs = get_probs(dataset)\n",
+    "\n",
+    "n = len(relevant_warehouses)\n",
+    "m = len(demand)\n",
+    "a = []\n",
+    "\n",
+    "# Create an array of driving times based on the df_distance DataFrame\n",
+    "t = [int(row['drivingTime_hrs']) for i, row in df_distance.iterrows()]\n",
+    "c=pd.DataFrame(t)\n",
+    "\n",
+    "# Amount to take per Warehouse\n",
+    "y = model.addVars(m, n, vtype=GRB.INTEGER, name=\"Warehouse_Allocation\")\n",
+    "\n",
+    "# Add constraints to meet demand for each disaster scenario (k)\n",
+    "for k in range(m):\n",
+    "    # Demand constraints\n",
+    "    model.addConstr(gp.quicksum(y[k, i] for i in range(n)) == demand[k], name=f\"Meet_Demand_K:{k}\")\n",
+    "    \n",
+    "    # Warehouse constraints\n",
+    "    for i, supplies in enumerate(relevant_warehouses):\n",
+    "        model.addConstr(y[k, i] <= supplies[2], name=f\"warehouse_endowment_K:{k}_I:{i}\")\n",
+    "\n",
+    "# Objective function to minimize the weighted driving time using T as a parameter\n",
+    "objective = gp.quicksum(\n",
+    "    probs[k] * gp.quicksum(t[i] * y[k, i] for i in range(n))\n",
+    "    for k in range(m)\n",
+    ")\n",
+    "\n",
+    "# Optimize model\n",
+    "model.setObjective(objective, GRB.MINIMIZE)\n",
+    "model.optimize()\n",
+    "\n",
+    "# Store results in the list 'a'\n",
+    "for v in model.getVars():\n",
+    "    a.append([v.VarName, v.X])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Warehouse_Allocation[0]</td>\n",
+       "      <td>375.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Warehouse_Allocation[1]</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Warehouse_Allocation[2]</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Warehouse_Allocation[3]</td>\n",
+       "      <td>2322.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Warehouse_Allocation[4]</td>\n",
+       "      <td>9046.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Warehouse_Allocation[5]</td>\n",
+       "      <td>610.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Warehouse_Allocation[6]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Warehouse_Allocation[7]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Warehouse_Allocation[8]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Warehouse_Allocation[9]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Warehouse_Allocation[10]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Warehouse_Allocation[11]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Warehouse_Allocation[12]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Warehouse_Allocation[13]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Warehouse_Allocation[14]</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           0       1\n",
+       "0    Warehouse_Allocation[0]   375.0\n",
+       "1    Warehouse_Allocation[1]    26.0\n",
+       "2    Warehouse_Allocation[2]    41.0\n",
+       "3    Warehouse_Allocation[3]  2322.0\n",
+       "4    Warehouse_Allocation[4]  9046.0\n",
+       "5    Warehouse_Allocation[5]   610.0\n",
+       "6    Warehouse_Allocation[6]    -0.0\n",
+       "7    Warehouse_Allocation[7]    -0.0\n",
+       "8    Warehouse_Allocation[8]    -0.0\n",
+       "9    Warehouse_Allocation[9]    -0.0\n",
+       "10  Warehouse_Allocation[10]    -0.0\n",
+       "11  Warehouse_Allocation[11]    -0.0\n",
+       "12  Warehouse_Allocation[12]    -0.0\n",
+       "13  Warehouse_Allocation[13]    -0.0\n",
+       "14  Warehouse_Allocation[14]    -0.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(a).iloc[:15]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40811"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5 Data Science Extension"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've seen how the equation uses the uniform distribution to solve the problem, but what if we knew something it didn't? What if knowing that climate change is increasingly energizing large storms, we decide the past hurricane impacts aren't representative of what's to come? In this section we want to prompt you to come up with predictive elements to improve our models. Feel free to use some of the ideas below or go in an entirely new direction!\n",
+    "\n",
+    "In this section, we encourage you to think creatively about enhancing predictive models for climate-related disasters. Consider how to incorporate novel data sources, feature engineering techniques, and model architectures to improve predictions. Below are some suggested approaches, but feel free to explore entirely new directions!\n",
+    "\n",
+    "Case Study Focus: Coastal Eastern African Nations\n",
+    "\n",
+    "Using the disaster impact data for coastal Eastern African nations, can you develop a model to predict how these impacts might escalate for Madagascar in the coming years? Consider not only the historical data but also factors such as changes in sea surface temperatures, shifting storm tracks, population growth along vulnerable coastlines, and evolving infrastructure resilience. Further, can you integrate this predictive model into an optimization framework to better allocate resources for disaster preparedness and response?\n",
+    "\n",
+    "Potential Approaches to Explore:\n",
+    "1. **Comparing Time Series Models:** Traditional statistical time series models like ARIMAX (AutoRegressive Integrated Moving Average with Explanatory Variables) are commonly used to predict future values based on past data. How do these models compare with more advanced Recurrent Neural Network (RNN)-based approaches like Long Short-Term Memory (LSTM) networks or Gated Recurrent Units (GRUs) in capturing long-term dependencies, especially under non-stationary conditions induced by climate change?\n",
+    "\n",
+    "2.\t**Incorporating Geospatial Data:** Geospatial features, such as latitude, longitude, elevation, and proximity to bodies of water, can play a crucial role in predicting the impact of tropical storms. Can we encode geospatial information using techniques like convolutional neural networks (CNNs) for spatial feature extraction, or leverage more specialized models such as Geographical Weighted Regression (GWR) or Graph Neural Networks (GNNs) to account for spatial dependencies?\n",
+    "\n",
+    "3.\t**Incorporating Climate Change Projections:** Beyond just historical data, consider how future climate projections can be integrated into the model. Can we use downscaled climate model outputs or ensemble approaches to account for different climate scenarios? How would these scenarios affect the frequency and intensity of tropical storms affecting coastal Eastern African nations?\n",
+    "\n",
+    "4.\t**Feature Engineering with Climate Indicators:** Introduce climate change indicators as predictive features. For example, how do trends in sea surface temperatures (SSTs), El Niño-Southern Oscillation (ENSO) phases, or the Atlantic Multi-decadal Oscillation (AMO) correlate with storm intensification? Would incorporating these indicators as additional time series variables enhance predictive accuracy?\n",
+    "\t\n",
+    "5.\t**Hybrid and Ensemble Models:** Can we leverage hybrid models that combine both statistical and deep learning approaches, or use ensemble methods that aggregate predictions from multiple models? For example, combining ARIMAX for short-term predictions with LSTM for capturing long-term trends may provide a more comprehensive forecasting tool.\n",
+    "\n",
+    "6.\t**Optimization Integration:** Once a reliable predictive model is established, how can it be integrated into an optimization framework for resource allocation? For example, can we build an optimization model that minimizes both the cost of disaster preparedness and the potential loss from future storm impacts?\n",
+    "\n",
+    "7.\t**Model Explainability and Decision-Making:** How can we ensure that the model is interpretable for decision-makers? Consider the use of techniques like SHAP (SHapley Additive exPlanations) values or LIME (Local Interpretable Model-agnostic Explanations) to explain which factors contribute most to the model’s predictions, helping policymakers make informed decisions.\n",
+    "\n",
+    "By combining predictive analytics with optimization, we can not only forecast future disaster impacts but also develop actionable strategies for minimizing those impacts. The goal is to make our models both more accurate and more useful in real-world applications, driving better outcomes for communities at risk.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6 Putting it All Together: Optimizing Allocation and Transportation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, now that we can solve for the time it takes to address every single disaster, we can finally answer the original question posed: how do we best allocate supplies to all the warehouses? The key here is to create a second optimization problem. Remember how in our constraints we included that you can't send more than the warehouse has? \n",
+    "\n",
+    "$$\n",
+    "y^k_i       \\leq x_i,\\space  \\forall i \\in I \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
+    "$$\n",
+    "\n",
+    "So far, we've just been using the actual allocation we have at each warehouse for $x_i$. But what if those changes? Suddenly we would have an entirely new solution. So, if we say that the output of Gurobi (i.e. the allocations $y^k_i \\in Y$) is some function based on our starting amount warehouses ($X$ where $x_i\\in X$), then we can say\n",
+    "\n",
+    "$$Y=f(X)$$ \n",
+    "\n",
+    "And when we frame it this way, it becomes much more simple to solve! All we need to do is minimize the travel times $Y$. In other words, our problem becomes \n",
+    "\n",
+    "\n",
+    "$$ \\min_{X} f(X) $$\n",
+    "$$\\begin{aligned}\n",
+    "\\text{s.t.}  & \\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n",
+    "\n",
+    " && x_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n",
+    "\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "Where $\\chi$ is the total amount of supplies we have in the country\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While this may initially look intimidating, it is one of the easiest changes to make to our current code. All we are doing is making a decision variable instead of a constant and then constraining it. Let’s substitute back in our equation from the last section with the new constraints to see this firsthand. To denote a new decision variable (i.e. a variable that can be changed), all we need to do is add it under the minimization sign. This means minimizing with respect to $x$ and $y$\n",
+    "\n",
+    "$$\n",
+    "\\min_{x,y} \\sum_k P^k \\sum_i \\tau_{ij}\\cdot y^k_i\n",
+    "$$\n",
+    "\n",
+    "Then all we need to do is update the constraints. I've included the line to make it easier to see what's new as our list grows. It has no mathematical significance. \n",
+    "\n",
+    "So how do we Implement this in Gurobi?\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\n",
+    "\n",
+    "\\text{s.t.}  & \\sum_{i} & y^k_{i}&=d^k & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n",
+    "\n",
+    "& & y^k_i       &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
+    "\n",
+    " &\\text{} & y^k_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n",
+    "\\hline \\\\\n",
+    "  & \\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n",
+    "\n",
+    " && x_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n",
+    "\n",
+    "\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So how do we Implment this in Gurobi?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n",
+      "\n",
+      "CPU model: Apple M1\n",
+      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
+      "\n",
+      "Optimize a model with 1153 rows, 1040 columns and 2192 nonzeros\n",
+      "Model fingerprint: 0x0e8cfb4c\n",
+      "Variable types: 0 continuous, 1040 integer (0 binary)\n",
+      "Coefficient statistics:\n",
+      "  Matrix range     [1e+00, 1e+00]\n",
+      "  Objective range  [9e-02, 4e-01]\n",
+      "  Bounds range     [0e+00, 0e+00]\n",
+      "  RHS range        [3e+00, 4e+04]\n",
+      "Presolve removed 1152 rows and 1026 columns\n",
+      "Presolve time: 0.02s\n",
+      "Presolved: 1 rows, 14 columns, 14 nonzeros\n",
+      "Variable types: 0 continuous, 14 integer (0 binary)\n",
+      "Found heuristic solution: objective 330422.47266\n",
+      "\n",
+      "Root relaxation: objective 3.274030e+05, 1 iterations, 0.00 seconds (0.00 work units)\n",
+      "\n",
+      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
+      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
+      "\n",
+      "*    0     0               0    327403.00000 327403.000  0.00%     -    0s\n",
+      "\n",
+      "Explored 1 nodes (1 simplex iterations) in 0.04 seconds (0.00 work units)\n",
+      "Thread count was 8 (of 8 available processors)\n",
+      "\n",
+      "Solution count 2: 327403 330422 \n",
+      "\n",
+      "Optimal solution found (tolerance 1.00e-04)\n",
+      "Best objective 3.274030000000e+05, best bound 3.274030000000e+05, gap 0.0000%\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = gp.Model(\"full_allocation\")\n",
+    "\n",
+    "n = len(relevant_warehouses)\n",
+    "m = len(demand)\n",
+    "\n",
+    "# Create an array of driving times based on the df_distance DataFrame\n",
+    "t = df_distance.drivingTime_hrs\n",
+    "\n",
+    "# Amount to take per Warehouse\n",
+    "y = model.addVars(m, n, vtype=GRB.INTEGER, name=\"Single_Warehouse_Allocation\")\n",
+    "\n",
+    "# National Allocation\n",
+    "X = model.addVars(n, vtype=GRB.INTEGER, name=\"National_Allocation\")\n",
+    "\n",
+    "# Total national endowment constraint\n",
+    "model.addConstr(gp.quicksum(X[i] for i in range(n)) == 40811, name=\"Total_National_Endowment\")\n",
+    "\n",
+    "for k in range(m):\n",
+    "    model.addConstr(y[k, i] <= X[i])\n",
+    "\n",
+    "# Demand and warehouse constraints for each scenario\n",
+    "for k in range(m):\n",
+    "    model.addConstr(gp.quicksum(y[k, i] for i in range(n)) == demand[k], name=f\"Meet_Demand_K:{k}\")\n",
+    "    for i, supplies in enumerate(relevant_warehouses):\n",
+    "        model.addConstr(y[k, i] <= supplies[2], name=f\"warehouse_endowment_K:{k}_I:{i}\")\n",
+    "\n",
+    "# Objective function to minimize the weighted driving time using T as a parameter\n",
+    "objective = gp.quicksum(\n",
+    "    probs[k] * gp.quicksum(t[i] * y[k, i] for i in range(n))\n",
+    "    for k in range(m)\n",
+    ")\n",
+    "\n",
+    "# Optimize model\n",
+    "model.setObjective(objective, GRB.MINIMIZE)\n",
+    "model.optimize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.7 A Few Final Tidbits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have our overall framework, we can extend it fairly easily to better model real-life scenarios. Let's look at two final pieces of the puzzle that ESUPS considers in their model:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Cost**\n",
+    "\n",
+    "Along with how long it takes to get items to a disaster relief site, it's also important to consider the cost to accomplish it. It might be a few hours faster to charter a jet to deliver blankets in the aftermath of a disaster, however, if it is 100x more expensive than by truck, that may constrain the organization from buying more blankets, chartering more trucks, or making it difficult to resupply for future disasters. So just as we solve for ways to minimize time, it can be important for firms with limited resources to make sure their money is being used to do the most good it can.\n",
+    "\n",
+    "So how do we do this? It's fairly simple. Our time matrix, which we've been using to show how close or far buildings are from the disaster relief site, is just a set of predefined weights/discounts. So, if we change the numbers to reflect the cost of transit, then suddenly we're solving a cost-minimization problem! In fact, the substitution is so one-to-one, that besides switching $\\tau_{ij}$ for $c_{ij}$, we don't have to change the equation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Travel Mode**\n",
+    "\n",
+    "The second additional facet considered in our real-life model that we haven't encountered yet is transportation mode. We alluded to it a little in the cost section, but often there is the option to fly or ship goods into a region, which can be especially useful when far away or the roads are clogged or otherwise unusable (such is often the case after a disaster).\n",
+    "\n",
+    "So how do we implement this? Well let's take a look back at $y_i^k$, our variable which says how many goods to send from warehouse $i$ to disaster $k$. All we want to do is reflect and updated description: how many goods to send from warehouse $i$ to disaster $k$ via mode $r$. This can easily be represented as $y_{ir}^k$, let's explain what's happened. Before is $y$ was an array of length $K$ with each index holding sub array of length $I$ (which we could also write as size $K \\times I$), now each index in our subarrays also have an array of length $3$ to represent how much is sent via truck, plane, or boat. So our final array is of dimensions $K \\times I \\times R $. This may seem intimidating at first, but remember, adding a dimension just means adding one more nested for loop!\n",
+    "\n",
+    "Let's look at how we would implement this. Remember, from a math point of view, all we've done is say $y_i^k$ can be broken down into $3$ modes instead of 1. So, it's rewritten as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "$$\n",
+    "\\min_{X,Y} \\sum_k P^k \\sum_i \\sum_r \\tau_{irj}\\cdot y^k_{ir}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\text{s.t.}  \\\\\n",
+    "\\sum_{i}\\sum_{r} & y^k_{ir}&=d^k & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n",
+    " \\sum_{r}  & y^k_{ir}       &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n",
+    " \\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n",
+    "\\text{} & y^k_{ir} &\\geq 0 &\\forall r\\in R, i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n",
+    "\\\\\n",
+    "& x_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n",
+    "\n",
+    "\n",
+    "\\end{aligned}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've added a few more sums here, but remember, in math, a sum is just a for loop. $\\sum_r$ is the equivalent to `for r in R:`. So how would we implement it in the format we've been using so far? This is going to be left as an open-ended exercise to the reader! If you get stuck, you can reference the production solver we'll be exploring below, which includes the mode of travel but is set up in a different approach than we've been using so far!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Open-Ended Implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.7 Interpreting the Solution "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So let's look at how much slower our real life allocations are in comparison to the optimal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Enhancing System Performance with the Balance Metric\n",
+    "\n",
+    "In humanitarian logistics, the efficiency of inventory allocation directly impacts the ability to respond swiftly and cost-effectively to disasters. The **Balance Metric** $(D)$ is a critical tool developed to evaluate the alignment of current inventory distribution with an optimal allocation. This metric is particularly valuable in contexts where multiple organizations independently manage inventory across various depots, without a centralized coordination mechanism.\n",
+    "\n",
+    "##### Definition and Calculation of the Balance Metric\n",
+    "\n",
+    "The balance metric $(D)$ is defined as the ratio between the actual objective value (either cost or time) of the current inventory allocation $V(X)$ and the optimal objective value $V(A')$ given the same overall capacity:\n",
+    "\n",
+    "$$ D = \\frac{V(A)}{V(A')}  $$\n",
+    "\n",
+    "Here:\n",
+    "-  $V(A)$: Represents the current system-wide cost or time to meet demand based on the existing inventory allocation \\(X\\).\n",
+    "-  $V(A')$: Represents the minimized cost or time if the inventory were optimally distributed across all depots.\n",
+    "\n",
+    "##### Interpretation of the Balance Metric\n",
+    "\n",
+    "1. **Optimal Inventory Allocation**:\n",
+    "   The optimal value of $D$ is 1. This occurs when the current allocation perfectly aligns with the optimal allocation, meaning no further reallocation could reduce costs or response times.\n",
+    "\n",
+    "2. **Identifying Imbalances**:\n",
+    "   When $D > 1$, the system is considered \"out-of-balance.\" A value of 1.2, for example, implies that the current allocation incurs 20% higher costs or longer response times compared to an optimal arrangement. This indicates a potential for improvement by reallocating resources more effectively.\n",
+    "\n",
+    "3. **Guiding System Improvements**:\n",
+    "   The balance metric is not only an indicator of inefficiency but also a guide for decision-making. By identifying locations or items with the highest imbalance, decision-makers can prioritize inventory reallocations that would yield the most significant improvements in terms of cost savings or faster response times.\n",
+    "\n",
+    "##### Practical Applications in Humanitarian Logistics\n",
+    "\n",
+    "The balance metric offers several practical applications for optimizing humanitarian response efforts:\n",
+    "\n",
+    "- **Strategic Reallocation of Resources**:\n",
+    "  Organizations can use the balance metric to identify under-stocked or over-stocked depots and adjust inventory levels accordingly. This strategic reallocation can significantly enhance response times or reduce costs, especially in multi-organizational contexts where coordination is limited.\n",
+    "\n",
+    "- **Sensitivity to Network Changes**:\n",
+    "  The balance metric is responsive to changes in the logistics network. For example, if a new depot is added in a high-risk area and remains under-stocked, the balance metric will reflect this imbalance, prompting an assessment of whether inventory should be redistributed to better leverage the new depot.\n",
+    "\n",
+    "- **Decision-Making in Real-Time Operations**:\n",
+    "  By continuously monitoring the balance metric as part of a real-time dashboard, operational managers can be alerted to changes that may impact overall system performance. This enables them to make data-driven decisions quickly, improving the overall resilience and responsiveness of the humanitarian supply chain.\n",
+    "\n",
+    "##### Limitations and Considerations\n",
+    "\n",
+    "While the balance metric provides valuable insights into inventory allocation efficiency, it is important to consider its limitations:\n",
+    "\n",
+    "- **Impact of Extreme Events**:\n",
+    "  The balance metric can be influenced by extreme scenarios, such as very large-scale disasters that significantly impact the calculated demand. As a result, it should be interpreted alongside other metrics, such as the fraction of demand served ($g$) or the weighted fraction of disasters completely served ($d$), to provide a more comprehensive picture of system performance.\n",
+    "\n",
+    "- **Dependence on Data Quality and Model Assumptions**:\n",
+    "  The accuracy of the balance metric depends on the quality of input data and the assumptions made in the model. Ensuring robust and accurate data collection processes and regularly updating model parameters to reflect real-world conditions are essential for maintaining the reliability of the metric.\n",
+    "\n",
+    "##### Conclusion\n",
+    "\n",
+    "The balance metric $D$ offers a powerful tool for evaluating and improving the efficiency of inventory allocation in humanitarian logistics. By identifying imbalances and guiding strategic reallocation decisions, this metric can help organizations optimize their response efforts, ensuring that resources are used most effectively to meet the needs of affected populations during disasters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='End'></a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gurobi_ml",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/optimization202/ESUPS_case_study/setup_imports.py b/optimization202/ESUPS_case_study/setup_imports.py
new file mode 100644
index 0000000..740c6d4
--- /dev/null
+++ b/optimization202/ESUPS_case_study/setup_imports.py
@@ -0,0 +1,397 @@
+from src.analysis import AnalysisParameters, Analyzer
+from src.dashboard_utils.dashboard_files import (
+    calc_exisiting_stock_df,
+    create_disaster_totals,
+    create_priority_change,
+    create_province_assess_df,
+    item_stock_assess,
+    reallocation_dashboard_files,
+)
+from src.dashboard_utils.dashboard_utils import get_scenario
+from src.dashboard_utils.dashboard_value_objects import (
+    BalMetricsDashboard,
+    ItemProvinceAssessDF,
+    ProvinceAssessDF,
+    ProvinceLookupDF,
+)
+from src.dashboard_utils.dauls_utils import calc_duals_by_warehouse
+from src.path import DASHBOARD_OUTPUT_PATH, DATA_DIR
+from src.reading import CsvProblemReader
+
+
+from IPython.display import Image
+import ipywidgets as widgets
+import sys
+from IPython.display import display
+from IPython.display import clear_output
+
+import ipywidgets as widgets
+from IPython.display import display, clear_output
+import warnings
+import pandas as pd
+
+import matplotlib.pyplot as plt
+import networkx as nx
+
+import plotly.express as px
+import numpy as np
+
+def question_1():
+    out = widgets.Output()
+
+    alternativ = widgets.RadioButtons(
+        options=[('w+f', 1), 
+                 ('3w+5f', 2),
+                 ('2w+4f', 3)],
+        description='',
+        disabled=False
+    )
+
+    check = widgets.Button(description="Check")
+
+    def sjekksvar(b):
+        a = int(alternativ.value)
+        right_answer = 2
+        if a == right_answer: 
+            color = '\x1b[6;30;42m' + "Correct! We're trying to maximize the relief we can provide by adding \n more or fewer items, and this equation describes the relief each item will add." + '\x1b[0m' + "\n"  # green color
+        else:
+            if a == 1:
+                color = '\x1b[5;30;41m' + "Not Quite. Remember that the value these items provide isn't the same." + '\x1b[0m' + "\n"  # red color
+            if a == 3:
+                color = '\x1b[5;30;41m' + "Not Quite. These are the constraints. What are we trying to maximize or minimize?" + '\x1b[0m' + "\n"  # red color
+        
+        with out:
+            clear_output()
+            print(color)
+    
+    print('\033[1m', '1) What is the Objective Function?', '\033[0m')
+    display(alternativ)
+    display(check)
+    display(out)
+
+    check.on_click(sjekksvar)
+
+def question_2():
+    out = widgets.Output()
+
+    alternativ = widgets.RadioButtons(
+        options=[('2w+4f', 1), 
+                 ('w+f', 2),
+                 ('3w+5f', 3)],
+        description='',
+        disabled=False
+    )
+
+    check = widgets.Button(description="Check")
+
+    def sjekksvar(b):
+        a = int(alternativ.value)
+        right_answer = 1
+        if a == right_answer: 
+            color = '\x1b[6;30;42m' + "Correct! This equation represents the cargo space constraint,\n which is based on the space each item takes up in the vehicle." + '\x1b[0m' + "\n"  # green color
+        else:
+            if a == 2:
+                color = '\x1b[5;30;41m' + "Not Quite. This could be an objective function, but it doesn't represent the cargo space constraint." + '\x1b[0m' + "\n"  # red color
+            if a == 3:
+                color = '\x1b[5;30;41m' + "Not Quite. This is the objective function, not the constraint related to cargo space." + '\x1b[0m' + "\n"  # red color
+        
+        with out:
+            clear_output()
+            print(color)
+    
+    print('\033[1m', '2) Which equation represents the cargo space constraint?', '\033[0m')
+    display(alternativ)
+    display(check)
+    display(out)
+
+    check.on_click(sjekksvar)
+
+def question_3():
+    out = widgets.Output()
+
+    alternativ = widgets.RadioButtons(
+        options=[('f <= 30', 1), 
+                 ('w + f <= 50', 2),
+                 ('2w + 4f <= 100', 3)],
+        description='',
+        disabled=False
+    )
+
+    check = widgets.Button(description="Check")
+
+    def sjekksvar(b):
+        a = int(alternativ.value)
+        right_answer = 1
+        if a == right_answer: 
+            color = '\x1b[6;30;42m' + "Correct! This equation represents the maximum number of food packs that can be delivered due to supply chain limitations." + '\x1b[0m' + "\n"  # green color
+        else:
+            if a == 2:
+                color = '\x1b[5;30;41m' + "Not Quite. This could represent a combined constraint, but it doesn't capture the limit on food packs." + '\x1b[0m' + "\n"  # red color
+            if a == 3:
+                color = '\x1b[5;30;41m' + "Not Quite. This is the cargo space constraint, not the limit on food packs." + '\x1b[0m' + "\n"  # red color
+        
+        with out:
+            clear_output()
+            print(color)
+    
+    print('\033[1m', '3) Which equation represents the limit on the number of food packs?', '\033[0m')
+    display(alternativ)
+    display(check)
+    display(out)
+
+    check.on_click(sjekksvar)
+
+# To test the questions, you can call each function in a Jupyter notebook cell.
+# question_1()
+# question_2()
+# question_3()
+
+def problem_1():
+    question_1()
+    question_2()
+    question_3()
+
+def get_distance_matrix(dataset):
+    #print('Disaster:',dataset.disasters[2].type.id)
+    BucketsNeeded=list(dataset.disaster_affected_totals.values())[2]
+    #print('Buckets Needed:',BucketsNeeded)
+
+    relevant_warehouses=[[key[0],key[1], dataset.inventory[key]] for key in dataset.inventory.keys() if key[1].id=='Buckets']
+    b=0
+    for i in relevant_warehouses:
+        b+=i[2]
+    #print('Bucket Avalible:',b)
+    #print(relevant_warehouses[0])
+    warehouse_location=sorted([a[0].id for a in relevant_warehouses])
+    disaster_location=dataset.disasters[2].impacted_locations[0].location.id
+    #print('Disaster Location:', disaster_location)
+
+
+    df=pd.read_csv('data/madagascar/distanceMatrix.csv')
+    df_filtered = df[df['depotGglAddressAscii'].isin(warehouse_location)]
+
+
+    df_filtered = df_filtered[df_filtered['disasterGglAddressAscii']==disaster_location]#.reset_index(drop=True)
+    #df_filtered
+    df_filtered.sort_values(by='depotCity',inplace=True)
+    df_filtered.reset_index(drop=True,inplace=True)
+
+    #print([x==y for x,y in zip(df_filtered['depotGglAddressAscii'],warehouse_location)])
+    return df_filtered, relevant_warehouses, BucketsNeeded
+
+def get_probs(dataset):
+    #len(dataset.probabilities)
+    dataset.disaster_affected_totals.values()
+    demand=[min(x,40811) for x in dataset.disaster_affected_totals.values()]
+    #print(demand)
+    #print(len(demand))
+    #print(len(relevant_warehouses))
+    probs=list(dataset.probabilities.values())
+    return demand, probs
+
+def flowchart():
+    # Define the nodes and edges of the flow diagram
+    nodes = ["CSV", "Data \nReader", "Data \nClass", "Analysis", "Worker", "Scope", "Problem \nInstance", "Solver"]
+    edges = [("CSV", "Data \nReader"), 
+            ("Data \nReader", "Data \nClass"), 
+            ("Data \nClass", "Analysis"), 
+            ("Analysis", "Worker"), 
+            ("Worker", "Scope"), 
+            ("Scope", "Problem \nInstance"), 
+            ("Problem \nInstance", "Solver"), 
+            ("Solver", "Analysis")]
+
+    # Create directed graph
+    G = nx.DiGraph()
+    G.add_nodes_from(nodes)
+    G.add_edges_from(edges)
+
+    # Define a new layout for a more organized appearance
+    pos = {
+        "CSV": (0, 3),
+        "Data \nReader": (1, 3),
+        "Data \nClass": (2, 3),
+        "Analysis": (3, 3),
+        "Worker": (3, 2),
+        "Scope": (3, 1),
+        "Problem \nInstance": (4, 1),
+        "Solver": (5, 1)
+    }
+
+    # Plot the graph with a more visually appealing layout and style
+    plt.figure(figsize=(12, 6))
+    nx.draw(G, pos, with_labels=True, 
+            node_color='#66c2a5', font_size=13, font_weight='bold', 
+            node_size=5400, arrowsize=20, edge_color='#1f78b4', 
+            edgecolors='black', linewidths=2)
+
+    # Add title and improve display
+    plt.title("Production Logical Flow", fontsize=14, fontweight='bold')
+    plt.axis('off')  # Hide axis
+    plt.show()
+
+
+def q1_p2():
+    # Output area for feedback
+    out1 = widgets.Output()
+
+    # Options for the first question
+    alternativ1 = widgets.RadioButtons(
+        options=[
+            ('Maximum demand for each type of item.', 1), 
+            ('Total time available on weaving and packaging machines.', 2),
+            ('The profit each pack of items generates.', 3),
+            ('The number of items that must be manufactured for quality control.', 4)
+        ],
+        description='',
+        disabled=False
+    )
+
+    # Button to check the answer
+    check1 = widgets.Button(description="Check")
+
+    # Function to check the answer for the first question
+    def sjekksvar1(b):
+        a = int(alternativ1.value)
+        right_answer = 4
+        if a == right_answer: 
+            color = '\x1b[6;30;42m' + "Correct! The constraint is that exactly 150 items must be manufactured for quality control." + '\x1b[0m' + "\n"  # green color
+        else:
+            if a == 1:
+                color = '\x1b[5;30;41m' + "Not Quite. The maximum demand tells us how much we can produce without exceeding what is needed, but it does not limit the total production to exactly 150 items." + '\x1b[0m' + "\n"  # red color
+            elif a == 2:
+                color = '\x1b[5;30;41m' + "Not Quite. The time available on machines restricts how long they can operate, but it doesn't limit the total number of items to exactly 150." + '\x1b[0m' + "\n"  # red color
+            elif a == 3:
+                color = '\x1b[5;30;41m' + "Not Quite. Profit is an objective to maximize, but it is not a constraint that limits production to exactly 150 items." + '\x1b[0m' + "\n"  # red color
+
+        with out1:
+            clear_output()
+            print(color)
+
+    # Display the first question
+    print('\033[1m', '1) What is the main constraint that limits the total production of items in this manufacturing setup?', '\033[0m')
+    display(alternativ1)
+    display(check1)
+    display(out1)
+
+    # Connect the button to the function
+    check1.on_click(sjekksvar1)
+
+def q2_p2():
+    # Output area for feedback
+    out2 = widgets.Output()
+
+    # Options for the second question
+    alternativ2 = widgets.RadioButtons(
+        options=[('w+f', 1), 
+                 ('3w+5f', 2),
+                 ('2w+4f', 3)],
+        description='',
+        disabled=False
+    )
+
+    # Button to check the answer
+    check2 = widgets.Button(description="Check")
+
+    # Function to check the answer for the second question
+    def sjekksvar2(b):
+        a = int(alternativ2.value)
+        right_answer = 3
+        if a == right_answer: 
+            color = '\x1b[6;30;42m' + "Correct! The objective is to maximize profit, which depends on the profit associated with each type of item." + '\x1b[0m' + "\n"  # green color
+        else:
+            if a == 1:
+                color = '\x1b[5;30;41m' + "Not Quite. The weaving and packaging time are constraints, but they do not directly impact the profit maximization objective." + '\x1b[0m' + "\n"  # red color
+            elif a == 2:
+                color = '\x1b[5;30;41m' + "Not Quite. The maximum demand limits production, but maximizing profit focuses on which items provide the highest profit, not their demand." + '\x1b[0m' + "\n"  # red color
+            elif a == 4:
+                color = '\x1b[5;30;41m' + "Not Quite. The total time available on machines is a constraint that limits production capacity, but maximizing profit is about choosing the most profitable items within that capacity." + '\x1b[0m' + "\n"  # red color
+
+        with out2:
+            clear_output()
+            print(color)
+
+        # Display the second question
+        print('\033[1m', '2) If the NGO wants to maximize the profit from the manufactured items, which factor should they primarily consider?', '\033[0m')
+        display(alternativ2)
+        display(check2)
+        display(out2)
+
+        # Connect the button to the function
+        check2.on_click(sjekksvar2)
+
+def problem_2():
+    q1_p2()
+    q2_p2()
+
+
+
+
+def q_3_1():
+    out = widgets.Output()
+
+    alternativ = widgets.RadioButtons(
+        options=[('-1', 1), 
+                 ('0', 2),
+                 ('1', 3)],
+        description='',
+        disabled=False
+    )
+
+    check = widgets.Button(description="Check")
+
+    def sjekksvar(b):
+        a = int(alternativ.value)
+        right_answer = 2
+        if a == right_answer: 
+            color = '\x1b[6;30;42m' + "Correct! We use the form probability times value so .5*(-1) + .5*(1) = 0 " + '\x1b[0m' + "\n"  # green color
+        else:
+            if a == 1:
+                color = '\x1b[5;30;41m' + "Not Quite. Remember we multiple each outcome with its probability then add them" + '\x1b[0m' + "\n"  # red color
+            elif a == 3:
+                color = '\x1b[5;30;41m' + "Not Quite. Remember we multiple each outcome with its probability then add them" + '\x1b[0m' + "\n"  # red color
+    
+        with out:
+            clear_output()
+            print(color)
+    
+    print('\033[1m', '1) What is the Objective Function?', '\033[0m')
+    display(alternativ)
+    display(check)
+    display(out)
+
+    check.on_click(sjekksvar)
+def q_3_2():
+    out = widgets.Output()
+
+    alternativ = widgets.RadioButtons(
+        options=[('0', 1), 
+                 ('1/2', 2),
+                 ('1', 3)],
+        description='',
+        disabled=False
+    )
+
+    check = widgets.Button(description="Check")
+
+    def sjekksvar(b):
+        a = int(alternativ.value)
+        right_answer = 3
+        if a == right_answer: 
+            color = '\x1b[6;30;42m' + "Correct! We use the form probability times value so .5*(5) + .5*(-3) = 4 " + '\x1b[0m' + "\n"  # green color
+        else:
+            if a == 1:
+                color = '\x1b[5;30;41m' + "Not Quite. Remember we multiple each outcome with its probability then add them" + '\x1b[0m' + "\n"  # red color
+            elif a == 2:
+                color = '\x1b[5;30;41m' + "Not Quite. Remember we multiple each outcome with its probability then add them" + '\x1b[0m' + "\n"  # red color
+    
+        with out:
+            clear_output()
+            print(color)
+    
+    print('\033[1m', '1) What is the Objective Function?', '\033[0m')
+    display(alternativ)
+    display(check)
+    display(out)
+
+    check.on_click(sjekksvar)
\ No newline at end of file
diff --git a/optimization202/ESUPS_case_study/src/__init__.py b/optimization202/ESUPS_case_study/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/optimization202/ESUPS_case_study/src/analysis.py b/optimization202/ESUPS_case_study/src/analysis.py
new file mode 100644
index 0000000..20af60e
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/analysis.py
@@ -0,0 +1,468 @@
+from enum import IntEnum
+from multiprocessing import Pool
+from os import getenv
+from typing import Callable
+
+import pandas as pd
+
+from src.data import Dataset, Disaster, DisasterImpact, DistanceInfo, Item
+from src.solving import (
+    AllocationStrategy,
+    CostMatrix,
+    Problem,
+    Solution,
+    SolverParameters,
+    StochasticSolver,
+)
+
+
+class SolverObjective(IntEnum):
+    Cost = (0,)
+    Time = (1,)
+    Distance = 2
+
+
+SolutionTags = tuple[SolverObjective, AllocationStrategy]
+
+
+class AnalysisParameters:
+    """
+
+    Attributes
+    ----------
+    expand_depot_set
+        Flag indicating whether inventory can be reallocated to depots that don't currently hold any stock
+    care_about_month_demand
+        Flag indicating whether we take month-by-month demand (True) or the general number (False)
+    disaster_month
+        Month from which to select disasters
+    num_months_to_average
+        Number of months to use for selecting disasters, when disasterMonth>=0
+    optimization_objectives
+        Set of objectives to use for running the optimization model
+    comparison_objectives
+        Set of objectives to use for comparing results
+    allocation_strategies
+        Which strategies to test for (re)allocation inventory to depots in the first stage
+    min_year
+        First year from which disasters should be taken into account
+    max_year
+        Last year from which disasters should be taken into account
+    scale_demand
+        Whether demand must be scaled to not exceed total available inventory or not
+
+
+    """
+
+    expand_depot_set: bool = False
+    care_about_month_demand: bool = True
+    disaster_month: int = -1
+    num_months_to_average: int = 3
+    optimization_objectives: list[SolverObjective] = [
+        SolverObjective.Cost,
+        SolverObjective.Time,
+    ]
+    comparison_objectives: list[SolverObjective] = list(SolverObjective)
+    allocation_strategies: list[AllocationStrategy] = list(AllocationStrategy)
+    min_year: int = 1900
+    max_year: int = 2100
+    scale_demand: bool = True
+
+
+class Analysis:
+    """
+    Analysis results for multiple optimization runs for a single dataset and item, using different objectives and allocation strategies.
+
+    Attributes
+    ----------
+    parameters:
+        Parameters used to construct the analysis
+    dataset:
+        Original dataset being analyzed
+    item:
+        Item for which the analysis was performed
+    solutions:
+        Dictionary of solutions for all solved problems
+    solution_stats
+        Index: objective, strategy
+        Columns:
+        - coveredDemandExcDummy
+        - dualTotInv
+        - totalCostIncDummy
+        - totalCostExcDummy
+        - totalDemand
+        - fractionOfDisastersUsingDummy
+        - averageUnitCost
+        - demandFulfillmentFraction
+    balance_metric
+        Index: objective
+        Columns: balanceMetric
+    units_shipped
+        Index: objective, strategy, mode
+        Columns: unitsShipped, unitsShippedWeighted
+    people_served_per_item
+        Index: objective, strategy
+        Columns: peopleServedPerItem
+    cross_ompact
+        Index: objective, strategy, other
+        Columns: impact
+    """
+
+    parameters: AnalysisParameters
+    dataset: Dataset
+    item: Item
+    solutions: dict[SolutionTags, Solution]
+    solution_stats: pd.DataFrame
+    balance_metric: pd.DataFrame
+    units_shipped: pd.DataFrame
+    people_served_per_item: pd.DataFrame
+    cross_impact: pd.DataFrame
+
+    def __init__(
+        self,
+        parameters: AnalysisParameters,
+        dataset: Dataset,
+        item: Item,
+        solutions: dict[SolutionTags, Solution],
+        solution_stats: pd.DataFrame,
+        balance_metric: pd.DataFrame,
+        units_shipped: pd.DataFrame,
+        people_served_per_item: pd.DataFrame,
+        cross_impact: pd.DataFrame,
+    ):
+        self.parameters = parameters
+        self.dataset = dataset
+        self.item = item
+        self.solutions = solutions
+        self.solution_stats = solution_stats
+        self.balance_metric = balance_metric
+        self.units_shipped = units_shipped
+        self.people_served_per_item = people_served_per_item
+        self.cross_impact = cross_impact
+
+
+class AnalyzerWorker:
+    def __init__(self, parameters: AnalysisParameters):
+        self.parameters = parameters
+        self._solver = StochasticSolver()
+
+    def run(self, dataset: Dataset, item: Item) -> Analysis:
+        #a=len(str(dataset))
+        dataset = self._filter_dataset(dataset)
+        #print(len(str(dataset))!=a)
+        probabilities = {
+            disaster: 1 / len(dataset.disasters) for disaster in dataset.disasters
+        }
+
+        solutions: dict[SolutionTags, Solution] = {}
+
+        # Construct cost matrices once
+        cost_matrices = {
+            objective: self._get_cost_matrix(dataset, item, objective)
+            for objective in SolverObjective
+        }
+
+        # Construct inventory
+        inventory = self._select_inventory(dataset, item)
+        if sum(inventory.values()) == 0:
+            return None
+
+        # Construct demand
+        demand = self._select_demand(dataset, item)
+
+        # Solve models for all objectives and strategies
+        for objective in self.parameters.optimization_objectives:
+            for strategy in self.parameters.allocation_strategies:
+                problem = Problem(
+                    dataset.depots,
+                    inventory,
+                    demand,
+                    dataset.disasters,
+                    probabilities,
+                    dataset.transport_modes,
+                    cost_matrices[objective],
+                )
+                parameters = SolverParameters(strategy, self.parameters.scale_demand)
+                tags = (objective, strategy)
+                solutions[tags] = self._solver.solve(problem, parameters)
+
+        return self._post_process(dataset, item, cost_matrices, solutions)
+
+    def dispose(self):
+        self._solver.dispose()
+
+    def _select_inventory(self, dataset: Dataset, item: Item):
+        return {
+            depot: dataset.inventory.get((depot, item), 0)
+            for depot in dataset.depots
+            if self.parameters.expand_depot_set
+            or dataset.inventory.get((depot, item), 0) > 0
+        }
+
+    def _filter_dataset(self, dataset: Dataset) -> Dataset:
+        if self.parameters.disaster_month > -1:
+            months = range(
+                self.parameters.disaster_month,
+                self.parameters.disaster_month
+                + 1
+                + self.parameters.num_months_to_average,
+            )
+            months = [(month - 1) % 12 + 1 for month in months]
+            predicate: Callable[[Disaster], bool] = (
+                lambda disaster: disaster.month in months
+            )
+            dataset = dataset.take_disaster_subset(predicate)
+
+        dataset = dataset.take_disaster_subset(
+            lambda disaster: disaster.year >= self.parameters.min_year
+            and disaster.year <= self.parameters.max_year
+        )
+
+        return dataset
+
+    def _select_demand(
+        self, dataset: Dataset, item: Item
+    ) -> dict[DisasterImpact, float]:
+        source = (
+            dataset.monthly_demand
+            if self.parameters.care_about_month_demand
+            else dataset.general_demand
+        )
+        return {
+            location: source.get((location, item), 0)
+            for disaster in dataset.disasters
+            for location in disaster.impacted_locations
+        }
+
+    def _get_cost_matrix(
+        self, dataset: Dataset, item: Item, objective: SolverObjective
+    ) -> CostMatrix:
+        return {
+            key: self._get_cost_element(value, objective, item)
+            for key, value in dataset.distance.items()
+        }
+
+    def _get_cost_element(
+        self, cell: DistanceInfo, objective: SolverObjective, item: Item
+    ):
+        if objective == SolverObjective.Cost:
+            return item.weight * cell.cost_per_ton
+        elif objective == SolverObjective.Time:
+            return cell.time
+        elif objective == SolverObjective.Distance:
+            return cell.distance
+        else:
+            raise RuntimeError(f"Undefined objective {objective}")
+
+    def _post_process(
+        self,
+        dataset: Dataset,
+        item: Item,
+        costs: dict[SolverObjective, CostMatrix],
+        solutions: dict[SolutionTags, Solution],
+    ):
+        beta_source = (
+            dataset.persons_per_item_monthly
+            if self.parameters.care_about_month_demand
+            else dataset.persons_per_item_general
+        )
+        beta = {
+            location.id: beta_source[location, item]
+            for disaster in dataset.disasters
+            for location in disaster.impacted_locations
+        }
+
+        solution_stats = pd.DataFrame.from_records(
+            [
+                {
+                    "objective": objective,
+                    "strategy": strategy,
+                    "coveredDemandExcDummy": solution.covered_demand_exc_dummy,
+                    "dualTotInv": solution.dual_total_inventory,
+                    "totalCostIncDummy": solution.total_cost_inc_dummy,
+                    "totalCostExcDummy": solution.total_cost_exc_dummy,
+                    "totalDemand": solution.total_demand,
+                    "fractionOfDisastersUsingDummy": solution.fraction_of_disasters_using_dummy,
+                }
+                for (objective, strategy), solution in solutions.items()
+            ]
+        ).set_index(["objective", "strategy"])
+
+        df_flows = pd.DataFrame.from_records(
+            [
+                {
+                    "objective": objective,
+                    "strategy": strategy,
+                    "disaster": disaster.id,
+                    "depot": depot.id,
+                    "impact": impact.id,
+                    "location": impact.location.id,
+                    "mode": mode.id,
+                    "flow": value,
+                    "distance": dataset.distance[depot, impact.location, mode].distance
+                    if depot.id != "DUMMY"
+                    else None,
+                }
+                for (objective, strategy), solution in solutions.items()
+                for (disaster, depot, impact, mode), value in solution.flow.items()
+            ]
+        )
+
+        # Average unit cost
+        solution_stats["averageUnitCost"] = solution_stats["totalCostExcDummy"] / (
+            solution_stats["coveredDemandExcDummy"] + 1e-7
+        )
+
+        # Demand fulfillment fraction
+        solution_stats["demandFulfillmentFraction"] = solution_stats[
+            "coveredDemandExcDummy"
+        ] / (solution_stats["totalDemand"] + 1e-7)
+
+        # Balance metric
+        strategies = set(solution_stats.reset_index()["strategy"])
+        pivoted = solution_stats.reset_index().pivot(
+            index="objective", columns="strategy", values="totalCostExcDummy"
+        )
+        pivoted["balanceMetric"] = (
+            pivoted[AllocationStrategy.MinimizeFixedInventory]
+            / (pivoted[AllocationStrategy.MinimizeTwoStage] + 1e-7)
+            if AllocationStrategy.MinimizeFixedInventory in strategies
+            and AllocationStrategy.MinimizeTwoStage in strategies
+            else None
+        )
+        balance_metric = pivoted
+
+        df_probabilities = pd.DataFrame.from_dict(
+            {
+                disaster.id: dataset.probabilities[disaster]
+                for disaster in dataset.disasters
+            },
+            columns=["probability"],
+            orient="index",
+        )
+
+        # Units shipped
+        df_flow_no_dummy = df_flows.join(df_probabilities, on="disaster")
+        df_flow_no_dummy = df_flow_no_dummy[
+            df_flow_no_dummy["depot"] != "DUMMY"
+        ]  # TODO Replace hardcoded dummy ID
+        temp = df_flow_no_dummy.copy()
+        temp["unitsShipped"] = temp["probability"] * temp["flow"]
+        temp["unitsShippedWeighted"] = temp["unitsShipped"] * temp["distance"]
+        units_shipped = (
+            temp.set_index(["objective", "strategy", "mode"])[
+                ["unitsShipped", "unitsShippedWeighted"]
+            ]
+            .groupby(["objective", "strategy", "mode"])
+            .sum()
+        )
+
+        # People served per item
+        temp = df_flow_no_dummy.copy()
+        temp["beta"] = temp["impact"].apply(lambda loc: beta[loc])
+        temp["peopleServed"] = temp["probability"] * temp["beta"] * temp["flow"]
+        people_served = (
+            temp.set_index(["objective", "strategy"])["peopleServed"]
+            .groupby(["objective", "strategy"])
+            .sum()
+        )
+        people_served_per_item = pd.DataFrame(
+            people_served / (solution_stats["coveredDemandExcDummy"] + 1e-7),
+            columns=["peopleServedPerItem"],
+        )
+
+        # Impact of optimizing one objective on another objective
+        impact = []
+        for other in self.parameters.comparison_objectives:
+            cost = {
+                (depot.id, location.id, mode.id): value
+                for (depot, location, mode), value in costs[other].items()
+            }
+            temp = df_flow_no_dummy.copy()
+            if temp.empty:
+                raise RuntimeError("Empty flow matrix encountered")
+            temp["cost"] = temp.apply(
+                lambda row: cost[row["depot"], row["location"], row["mode"]], axis=1
+            )
+            temp["other"] = other
+            temp["impact"] = temp["cost"] * temp["probability"] * temp["flow"]
+            impact.append(temp.reset_index())
+        cross_impact = (
+            pd.concat(impact)
+            .groupby(["objective", "strategy", "other"])[["impact"]]
+            .sum()
+        )
+
+        return Analysis(
+            self.parameters,
+            dataset,
+            item,
+            solutions,
+            solution_stats,
+            balance_metric,
+            units_shipped,
+            people_served_per_item,
+            cross_impact,
+        )
+
+
+class Analyzer:
+    """
+    Service responsible for performing optimization runs and analysis on the results
+    """
+
+    def __init__(self, parameters: AnalysisParameters):
+        self.parameters = parameters
+
+    def run(self, dataset: Dataset, item: Item) -> Analysis:
+        worker = AnalyzerWorker(self.parameters)
+        result = worker.run(dataset, item)
+        worker.dispose()
+        return result
+
+    def run_all(self, dataset: Dataset) -> dict[tuple[str, Item], Analysis]:
+        inventory_datasets = {
+            filename: dataset.take_inventory_scenario(filename)
+            for filename in dataset.inventory_scenarios
+        }
+        tasks = [
+            (filename, inventory_dataset, item)
+            for (filename, inventory_dataset) in inventory_datasets.items()
+            for item in inventory_dataset.items
+        ]
+
+        return self._run_tasks(tasks)
+
+    def _run_tasks(self, tasks: list[tuple[str, Dataset, Item]]):
+        use_multi_processing = getenv("CI", "false") == "false"
+        use_multi_processing = False
+        if use_multi_processing:
+            with Pool(
+                initializer=_analysis_worker_init, initargs=[self.parameters]
+            ) as pool:
+                result: list[tuple[str, Analysis]] = pool.map(
+                    _analysis_worker_call, tasks
+                )
+        else:
+            worker = AnalyzerWorker(self.parameters)
+            result = [
+                (filename, worker.run(dataset, item))
+                for (filename, dataset, item) in tasks
+            ]
+            worker.dispose()
+        return {
+            (filename, analysis.item): analysis
+            for (filename, analysis) in result
+            if analysis is not None
+        }
+
+
+def _analysis_worker_init(parameters):
+    global worker
+    worker = AnalyzerWorker(parameters)
+
+
+def _analysis_worker_call(arg: tuple[Dataset, Item]) -> tuple[str, Analysis]:
+    global worker
+    (filename, dataset, item) = arg
+    return (filename, worker.run(dataset, item))
diff --git a/optimization202/ESUPS_case_study/src/create_dashboard_files.py b/optimization202/ESUPS_case_study/src/create_dashboard_files.py
new file mode 100644
index 0000000..5f0396d
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/create_dashboard_files.py
@@ -0,0 +1,126 @@
+import pandas as pd
+
+from src.analysis import AnalysisParameters, Analyzer
+from src.dashboard_utils.dashboard_files import (
+    calc_exisiting_stock_df,
+    create_disaster_totals,
+    create_priority_change,
+    create_province_assess_df,
+    item_stock_assess,
+    reallocation_dashboard_files,
+)
+from src.dashboard_utils.dashboard_utils import get_scenario
+from src.dashboard_utils.dashboard_value_objects import (
+    BalMetricsDashboard,
+    ItemProvinceAssessDF,
+    ProvinceAssessDF,
+    ProvinceLookupDF,
+)
+from src.dashboard_utils.dauls_utils import calc_duals_by_warehouse
+from src.path import DASHBOARD_OUTPUT_PATH, DATA_DIR
+from src.reading import CsvProblemReader
+
+COUNTRY = "Vanuatu"
+
+# Run optimization
+reader = CsvProblemReader()
+dataset = reader.read(DATA_DIR / COUNTRY)
+parameters = AnalysisParameters()
+analyzer = Analyzer(parameters)
+result = analyzer.run_all(dataset)
+
+
+### ExisitingStockAssessDF dashboard file
+exisiting_stock_df = calc_exisiting_stock_df(dataset, country=COUNTRY)
+
+
+### Priority Change dashboard file
+priority_change_df = create_priority_change(result, country=COUNTRY)
+
+
+all_duals_df = pd.DataFrame()
+for key, value in result.items():
+    scenario = get_scenario(key)
+
+    if scenario != "actual":
+        continue
+
+    duals_df = calc_duals_by_warehouse(value)
+    all_duals_df = pd.concat([all_duals_df, duals_df], ignore_index=True)
+
+
+### ProvinceAssessDF dashboard file
+provinces_df = pd.read_csv(DATA_DIR / COUNTRY / "province_lookup.csv")
+provinces_df: pd.DataFrame = ProvinceLookupDF.validate(provinces_df)
+province_assess_df: pd.DataFrame = create_province_assess_df(
+    all_duals_df,
+    provinces_df,
+    COUNTRY,
+)
+
+### WhStockAssessDF dashboard file
+duals_df_copy = all_duals_df.copy()
+wh_stock_assess: pd.DataFrame = item_stock_assess(duals_df_copy, provinces_df, COUNTRY)
+
+
+### Reallocation dashboard file
+reallocation_df, single_warehouse_df = reallocation_dashboard_files(
+    wh_stock_assess.copy(),
+    result,
+    COUNTRY,
+    wh_stock_assess=wh_stock_assess,
+)
+
+### Disaster totals dashboard file
+dis_totals_df = create_disaster_totals(dataset, COUNTRY)
+
+
+### Save dashboard files
+
+if not (DASHBOARD_OUTPUT_PATH / COUNTRY).exists():
+    (DASHBOARD_OUTPUT_PATH / COUNTRY).mkdir(parents=True)
+
+exisiting_stock_df.to_csv(
+    DASHBOARD_OUTPUT_PATH / COUNTRY / "exisiting_stock.csv",
+    index=False,
+)
+
+priority_change_df = priority_change_df.loc[
+    priority_change_df[BalMetricsDashboard.run_pct] == "actual"
+]
+priority_change_df = priority_change_df.drop(columns=[BalMetricsDashboard.run_pct])
+priority_change_df.to_csv(
+    DASHBOARD_OUTPUT_PATH / COUNTRY / "priority_change.csv",
+    index=False,
+)
+
+province_assess_df = province_assess_df.drop(columns=[ProvinceAssessDF.time])
+province_assess_df.to_csv(
+    DASHBOARD_OUTPUT_PATH / COUNTRY / "province_assess.csv",
+    index=False,
+)
+
+dec_wh_stock_assess = wh_stock_assess.drop(columns=[ItemProvinceAssessDF.time_hms])
+dec_wh_stock_assess.to_csv(
+    DASHBOARD_OUTPUT_PATH / COUNTRY / "wh_stock_assess_as_decimal.csv",
+    index=False,
+)
+wh_stock_assess = wh_stock_assess.drop(columns=[ItemProvinceAssessDF.time_hms])
+wh_stock_assess.to_csv(
+    DASHBOARD_OUTPUT_PATH / COUNTRY / "wh_stock_assess.csv",
+    index=False,
+)
+
+reallocation_df.to_csv(
+    DASHBOARD_OUTPUT_PATH / COUNTRY / "reallocation.csv",
+    index=False,
+)
+single_warehouse_df.to_csv(
+    DASHBOARD_OUTPUT_PATH / COUNTRY / "single_warehouse.csv",
+    index=False,
+)
+
+dis_totals_df.to_csv(
+    DASHBOARD_OUTPUT_PATH / COUNTRY / "disaster_totals.csv",
+    index=False,
+)
diff --git a/optimization202/ESUPS_case_study/src/dashboard_utils/__init__.py b/optimization202/ESUPS_case_study/src/dashboard_utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_files.py b/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_files.py
new file mode 100644
index 0000000..a77bd66
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_files.py
@@ -0,0 +1,513 @@
+from itertools import product
+
+import numpy as np
+import pandas as pd
+import pandera.typing as pat
+
+from src.analysis import Analysis, SolverObjective
+from src.dashboard_utils.dashboard_utils import (
+    calc_bal_metric,
+    calc_increase_stock_pct,
+    create_scenario_combo,
+    determine_run_pcnt,
+    enough_stock,
+    frac_disaster_covered,
+    get_actual_inventory,
+    get_scenario_from_str,
+    map_warehouse_province,
+    order_by_schema,
+    quantile_exc,
+    to_hms,
+)
+from src.dashboard_utils.dashboard_value_objects import (
+    BalMetricsDashboard,
+    DisasterTotals,
+    DualsByWharehouseDF,
+    ExisitingStockAssessDF,
+    ExistingStockInfo,
+    ItemProvinceAssessDF,
+    OptimalStockDF,
+    ProvinceAssessDF,
+    ProvinceLookupDF,
+    ReallocationOptionsDF,
+    SingleWarehouseMoveDF,
+)
+from src.dashboard_utils.dauls_utils import (
+    OBJECTIVE_STR_MAP,
+    add_normalized_shadow_price,
+    create_mean_duals,
+)
+from src.dashboard_utils.reallocations_utils import (
+    create_admin1_act_inv,
+    reallocation_option_loop,
+)
+from src.data import Dataset, Item
+from src.path import DATA_DIR
+from src.solving import AllocationStrategy
+
+
+def create_optimal_stock_df(
+    opt_results: dict[tuple[str, Item], Analysis],
+    admin1_lookup: dict[str, str],
+) -> pat.DataFrame[OptimalStockDF]:
+    """Create the optimal stock dataframe for the given set of results.
+
+
+    For each item and location and scenario determines what the optimal stock level
+    is.
+
+    Args:
+        opt_results: A dictionary of results from the analysis. The key is a tuple of
+        the scenario and item. The value is the analysis result.
+        admin1_lookup: A dictionary of the warehouse id and the admin 1.
+
+    Returns:
+        A dataframe of the optimal stock for each item and location and scenario.
+
+    """
+    all_opt_inv_data = []
+    for key, value in opt_results.items():
+        scenario = get_scenario_from_str(key[0])
+
+        if scenario == "actual":
+            continue
+
+        item = key[1].id
+
+        solution = value.solutions[
+            SolverObjective.Cost,
+            AllocationStrategy.MinimizeTwoStage,
+        ]
+        optimal_inventory = solution.optimal_inventory
+
+        for warehouse, stock in optimal_inventory.items():
+            warehouse_id = warehouse.id.split(",")[0]
+            location = admin1_lookup[warehouse_id]
+
+            all_opt_inv_data.append(
+                {
+                    OptimalStockDF.item: item,
+                    OptimalStockDF.percentile: scenario,
+                    OptimalStockDF.warehouse_id: warehouse_id,
+                    OptimalStockDF.optimal_stock: stock,
+                    OptimalStockDF.location: location,
+                },
+            )
+
+    all_opt_inv_df = pd.DataFrame(all_opt_inv_data)
+    _ = OptimalStockDF.validate(all_opt_inv_df)
+    return all_opt_inv_df
+
+
+def calc_exisiting_stock_df(
+    dataset: Dataset,
+    country: str,
+) -> pat.DataFrame[ExisitingStockAssessDF]:
+    """Calculate the existing stock dataframe.
+
+    Args:
+        dataset: The dataset.
+        country: The country the analysis is for.
+
+    Returns:
+        The existing stock dataframe.
+
+    """
+
+    exisiting_info = calc_exisiting_stock_info(dataset)
+
+    # Format data for df
+    df_start = [
+        (key[0], key[1], value)
+        for key, value in exisiting_info.increase_stock_pct.items()
+    ]
+    exisiting_stock_df = pd.DataFrame(
+        df_start,
+        columns=[
+            ExisitingStockAssessDF.item,
+            ExisitingStockAssessDF.scenario,
+            ExisitingStockAssessDF.increase_stock_pct,
+        ],
+    )
+
+    # Map item to required info for the df
+    exisiting_stock_df[ExisitingStockAssessDF.ppl_served_per_item] = exisiting_stock_df[
+        ExisitingStockAssessDF.item
+    ].map(exisiting_info.ppl_served_per_item)
+    exisiting_stock_df[
+        ExisitingStockAssessDF.frac_disaster_covered
+    ] = exisiting_stock_df[ExisitingStockAssessDF.item].map(
+        exisiting_info.frac_disaster_covered,
+    )
+    exisiting_stock_df[
+        ExisitingStockAssessDF.ppl_served_exisiting
+    ] = exisiting_stock_df[ExisitingStockAssessDF.item].map(
+        exisiting_info.ppl_served_exisiting,
+    )
+    exisiting_stock_df[ExisitingStockAssessDF.ppl_affected] = exisiting_stock_df[
+        ExisitingStockAssessDF.scenario
+    ].map(exisiting_info.scenario_disaster_size)
+
+    # Calculate additional columns for df
+    exisiting_stock_df[ExisitingStockAssessDF.enough_stock] = exisiting_stock_df.apply(
+        lambda row: exisiting_info.enough_stock[
+            (row[ExisitingStockAssessDF.item], row[ExisitingStockAssessDF.scenario])
+        ],
+        axis=1,
+    )
+    exisiting_stock_df[
+        ExisitingStockAssessDF.recommended_stock
+    ] = exisiting_stock_df.apply(
+        lambda row: exisiting_info.recommended_stock[
+            row[ExisitingStockAssessDF.scenario]
+        ][row[ExisitingStockAssessDF.item]],
+        axis=1,
+    )
+    exisiting_stock_df[ExisitingStockAssessDF.exisiting_inventory] = exisiting_stock_df[
+        ExisitingStockAssessDF.item
+    ].map(exisiting_info.initial_stock)
+
+    exisiting_stock_df[ExisitingStockAssessDF.country] = country
+
+    _ = ExisitingStockAssessDF.validate(exisiting_stock_df)
+
+    return order_by_schema(exisiting_stock_df, ExisitingStockAssessDF)
+
+
+def calc_exisiting_stock_info(dataset: Dataset) -> ExistingStockInfo:
+    """Calculate the required info which is used to create the existing stock df.
+
+    Args:
+        dataset: The dataset.
+
+    Returns:
+        The required info to create the existing stock df.
+
+    """
+
+    items = [item.id for item in dataset.items]
+
+    # Get the recommended inventory levels for each item at different percentiles
+    recommended_stock = get_actual_inventory(dataset)
+    initial_stock = recommended_stock.pop("actual")
+
+    # Get the people served by the existing inventory
+    ppl_per_item = {
+        key[1].id: value for key, value in dataset.persons_per_item_general.items()
+    }
+    ppl_served_exisiting = {
+        item: int(ppl_per_item[item] * initial_stock[item]) for item in items
+    }
+
+    scenarios = [get_scenario_from_str(i) for i in dataset.inventory_scenarios]
+    scenarios = [int(i) / 100 for i in scenarios if i != "actual"]
+
+    # Get the disaster sizes for each scenario
+    disaster_affected = dataset.disaster_affected_totals
+    disaster_sizes = [
+        quantile_exc(list(disaster_affected.values()), scenario)
+        for scenario in scenarios
+    ]
+    disaster_sizes = np.round(disaster_sizes, 0)
+    scenario_disaster_sizes = dict(zip(scenarios, disaster_sizes, strict=True))
+
+    # Get recommended stock for each scenario
+    recommended_stock = {}
+    for scenario in scenarios:
+        recommended_stock[scenario] = {
+            item: round(scenario_disaster_sizes[scenario] / ppl_per_item[item], 0)
+            for item in items
+        }
+
+    # Get the fraction of the disaster covered by the existing inventory
+    frac_disaster_cov = {
+        item: frac_disaster_covered(
+            ppl_served_exisiting[item],
+            list(disaster_affected.values()),
+        )
+        for item in items
+    }
+
+    enough_stock_lkup = {}
+    increase_stock_recommendations = {}
+    for item, scenario in product(items, scenarios):
+        enough_stock_lkup[(item, scenario)] = enough_stock(
+            item,
+            scenario,
+            initial_stock,
+            recommended_stock,
+        )
+
+        increase_stock_recommendations[(item, scenario)] = calc_increase_stock_pct(
+            item,
+            scenario,
+            initial_stock,
+            recommended_stock,
+        )
+
+    return ExistingStockInfo(
+        recommended_stock=recommended_stock,
+        initial_stock=initial_stock,
+        ppl_served_per_item=ppl_per_item,
+        scenario_disaster_size=scenario_disaster_sizes,
+        frac_disaster_covered=frac_disaster_cov,
+        enough_stock=enough_stock_lkup,
+        increase_stock_pct=increase_stock_recommendations,
+        ppl_served_exisiting=ppl_served_exisiting,
+    )
+
+
+def create_province_assess_df(
+    duals_warehouse_df: pat.DataFrame[DualsByWharehouseDF],
+    province_lookup_df: pat.DataFrame[ProvinceLookupDF],
+    country: str,
+) -> pat.DataFrame[ProvinceAssessDF]:
+    """Creates a dataframe of the province assessment dashboard file.
+
+    Args:
+        duals_warehouse_df: The dataframe containing the duals by warehouse
+        province_lookup_df: The dataframe containing the warehouse province lookup
+        country: The country the analysis is for.
+
+    Returns:
+        A dataframe of the province assessment dashboard file.
+
+    """
+
+    duals_df_copy = duals_warehouse_df.copy()
+    mean_raw_duals = create_mean_duals(duals_df_copy)
+    duals_df_copy = add_normalized_shadow_price(duals_df_copy, mean_raw_duals)
+
+    # Split out the warehouse id and map to province
+    duals_df_copy[ProvinceAssessDF.province] = map_warehouse_province(
+        province_lookup_df,
+        duals_df_copy,
+    )
+
+    # Calculate the mean normalized shadow price for each province
+    geo_province = duals_df_copy.groupby(
+        [DualsByWharehouseDF.objective, ProvinceAssessDF.province],
+    )["NormalizedShadowPrice"].agg("mean")
+
+    geo_province_df = pd.DataFrame(geo_province).reset_index()
+    geo_province_df = geo_province_df.pivot_table(
+        index=ProvinceAssessDF.province,
+        columns=DualsByWharehouseDF.objective,
+        values="NormalizedShadowPrice",
+    ).reset_index()
+
+    geo_province_df[ProvinceAssessDF.time_hms] = geo_province_df[
+        ProvinceAssessDF.time
+    ].apply(to_hms)
+
+    geo_province_df[ProvinceAssessDF.country] = country
+
+    _ = ProvinceAssessDF.validate(geo_province_df)
+    return order_by_schema(geo_province_df, ProvinceAssessDF)
+
+
+def create_priority_change(
+    result: dict[tuple[str, Item], Analysis],
+    country: str,
+) -> pat.DataFrame[BalMetricsDashboard]:
+    """Create the balance metric dataframe for the given set of results.
+
+    Args:
+        result: A dictionary of results from the analysis. The key is a tuple of
+        the scenario and item. The value is the analysis result.
+        country: The country the analysis is for.
+
+    Returns:
+        A dataframe of the balance metric for each item and scenario.
+
+    """
+
+    balance_metric_dfs = []
+    for key, value in result.items():
+        run_pct = determine_run_pcnt(key[0])
+        balance_metric_dfs.append(calc_bal_metric(run_pct, value))
+
+    balance_metrics = pd.concat(balance_metric_dfs, ignore_index=True)
+
+    balance_metrics[BalMetricsDashboard.item] = balance_metrics[
+        BalMetricsDashboard.item
+    ].apply(lambda item: item.id)
+    balance_metrics["objective"] = balance_metrics["objective"].apply(
+        lambda x: OBJECTIVE_STR_MAP[x],
+    )
+
+    balance_metrics = balance_metrics.pivot_table(
+        index=[BalMetricsDashboard.item, BalMetricsDashboard.run_pct],
+        columns="objective",
+        values="balanceMetric",
+    ).reset_index()
+
+    balance_metrics[BalMetricsDashboard.grand_total] = (
+        balance_metrics[BalMetricsDashboard.cost]
+        + balance_metrics[BalMetricsDashboard.time]
+    )
+    balance_metrics.columns.name = None
+    balance_metrics[BalMetricsDashboard.country] = country
+    _ = BalMetricsDashboard.validate(balance_metrics)
+    return order_by_schema(balance_metrics, BalMetricsDashboard)
+
+
+def item_stock_assess(
+    duals_wharehouse_df: pd.DataFrame,
+    province_lookup_df: pat.DataFrame[ProvinceLookupDF],
+    country: str,
+) -> pat.DataFrame[ItemProvinceAssessDF]:
+    """Calculates the cost and time savings for each province for all items for time
+    and cost objectives.
+
+    Args:
+        duals_wharehouse_df: The dataframe containing the duals by warehouse
+        province_lookup_df: The dataframe containing the warehouse province lookup
+        country: The country the analysis is for.
+
+    Returns:
+        Dataframe containing cost/time for all items and provinces.
+
+    """
+    item_stock_assess_df: pd.DataFrame = duals_wharehouse_df.groupby(
+        DualsByWharehouseDF.item_type,
+    ).apply(
+        create_province_assess_df,
+        province_lookup_df=province_lookup_df,
+        country=country,
+    )
+
+    # Handle multi-index
+    item_stock_assess_df = item_stock_assess_df.reset_index(
+        level=DualsByWharehouseDF.item_type,
+    ).reset_index(drop=True)
+
+    item_stock_assess_df[ItemProvinceAssessDF.country] = country
+    _ = ItemProvinceAssessDF.validate(item_stock_assess_df)
+    return order_by_schema(item_stock_assess_df, ItemProvinceAssessDF)
+
+
+def reallocation_dashboard_files(
+    geo_wh_stock_df: pat.DataFrame[ItemProvinceAssessDF],
+    opt_result: dict[tuple[str, Item], Analysis],
+    country: str,
+    wh_stock_assess: pat.DataFrame[ItemProvinceAssessDF],
+) -> tuple[pat.DataFrame[ReallocationOptionsDF], pat.DataFrame[SingleWarehouseMoveDF]]:
+    """Create the reallocation and single warehouse move from dataframes.
+
+    Args:
+        geo_wh_stock_df: The geo warehouse stock dataframe.
+        opt_result: The results from the optimization.
+        country: The country the analysis if for.
+        wh_stock_assess: The warehouse stock assessment dataframe.
+
+    Returns:
+        A tuple containing the reallocation options and single warehouse move from
+        dataframes.
+
+    """
+    provinces_df = pd.read_csv(DATA_DIR / country / "province_lookup.csv")
+    provinces_df: pd.DataFrame = ProvinceLookupDF.validate(provinces_df)
+    admin1_lookup = provinces_df.set_index(ProvinceLookupDF.warehouse_id).to_dict()[
+        ProvinceLookupDF.province
+    ]
+
+    # Get optimal stock levels
+    all_opt_inv_df = create_optimal_stock_df(opt_result, admin1_lookup)
+
+    starting_inventory = create_admin1_act_inv(
+        DATA_DIR / country / "inventory" / "actual.csv",
+        admin1_lookup,
+    )
+
+    item_percentile_lvl_df = (
+        all_opt_inv_df.groupby(
+            [OptimalStockDF.location, OptimalStockDF.percentile, OptimalStockDF.item],
+        )[OptimalStockDF.optimal_stock]
+        .sum()
+        .reset_index()
+    )
+    item_percentile_lvl_df = item_percentile_lvl_df.pivot_table(
+        index=[OptimalStockDF.location, OptimalStockDF.item],
+        columns=OptimalStockDF.percentile,
+        values=OptimalStockDF.optimal_stock,
+    ).reset_index()
+
+    item_percentile_lvl_df = item_percentile_lvl_df.merge(
+        starting_inventory,
+        on=[OptimalStockDF.location, OptimalStockDF.item],
+        how="left",
+    ).fillna(0)
+
+    # Create scenario combinations
+    user_location = all_opt_inv_df[OptimalStockDF.location].unique()
+    items = wh_stock_assess[ItemProvinceAssessDF.item_type].unique()
+    scenario_combinations = create_scenario_combo(user_location, items)
+    num_scenarios = len(list(create_scenario_combo(user_location, items)))
+
+    geo_wh_stock_df = geo_wh_stock_df.set_index(
+        [ItemProvinceAssessDF.province, ItemProvinceAssessDF.item_type],
+    )
+    geo_wh_stock_df = geo_wh_stock_df.drop(columns=[ItemProvinceAssessDF.time_hms])
+
+    reallocation_options_df, single_warehouse_df = reallocation_option_loop(
+        scenario_combinations,
+        num_scenarios,
+        item_percentile_lvl_df,
+        geo_wh_stock_df,
+        country=country,
+    )
+
+    reallocation_options_df[ReallocationOptionsDF.scenario] = reallocation_options_df[
+        ReallocationOptionsDF.scenario
+    ].astype(np.int64)
+
+    reallocation_options_df[ReallocationOptionsDF.country] = country
+
+    ord_reallocation_df = order_by_schema(
+        reallocation_options_df,
+        ReallocationOptionsDF,
+    )
+
+    single_warehouse_df = single_warehouse_df.drop_duplicates()
+    ord_reallocation_df[ReallocationOptionsDF.time] = ord_reallocation_df[
+        ReallocationOptionsDF.time
+    ].apply(to_hms)
+
+    ord_reallocation_df[ReallocationOptionsDF.move_from] = ord_reallocation_df[
+        ReallocationOptionsDF.move_from
+    ].apply(
+        lambda x: "Extra" if x else "Needed",
+    )
+
+    _ = ReallocationOptionsDF.validate(ord_reallocation_df)
+    _ = SingleWarehouseMoveDF.validate(single_warehouse_df)
+
+    return ord_reallocation_df, single_warehouse_df
+
+
+def create_disaster_totals(
+    dataset: Dataset,
+    country: str,
+) -> pat.DataFrame[DisasterTotals]:
+    """Create the disaster totals dataframe for a given country.
+
+    Args:
+        dataset: The dataset.
+        country: The country the analysis is for.
+
+    Returns:
+        The disaster totals dataframe.
+
+    """
+    totals_df = pd.DataFrame.from_dict(dataset.disaster_affected_totals, orient="index")
+    totals_df = totals_df.reset_index()
+    totals_df[DisasterTotals.country] = country
+    totals_df = totals_df.rename(
+        columns={"index": DisasterTotals.disaster_id, 0: DisasterTotals.total_affected},
+    )
+
+    totals_df = totals_df.sort_values(by=DisasterTotals.total_affected)
+
+    _ = DisasterTotals.validate(totals_df)
+    return totals_df
diff --git a/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_utils.py b/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_utils.py
new file mode 100644
index 0000000..e780f56
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_utils.py
@@ -0,0 +1,253 @@
+import re
+from collections.abc import Iterable
+from itertools import product
+
+import numpy as np
+import pandas as pd
+import pandera.typing as pat
+from pandera import DataFrameModel
+from scipy import interpolate
+from scipy.stats import rankdata
+
+from src.analysis import Analysis
+from src.dashboard_utils.dashboard_value_objects import (
+    BalMetricsDashboard,
+    DualsByWharehouseDF,
+    ProvinceLookupDF,
+)
+from src.data import Dataset, Item
+
+
+def to_hms(fraction: float) -> str:
+    """Takes in a fraction of a day an converts to hh:mm:ss.
+
+    Args:
+        fraction: Fraction of a day i.e 0.2 is 20% of a day
+
+    Returns:
+        str: String representing hours, minutes, seconds
+    """
+    negative = False
+    if fraction < 0:
+        fraction = fraction * -1
+        negative = True
+
+    as_hours = 24 * fraction
+    hours = int(as_hours)
+    minutes = (as_hours * 60) % 60
+    seconds = (as_hours * 3600) % 60
+
+    if negative:
+        return "-%d:%02d:%02d" % (hours, minutes, seconds)
+
+    return "%d:%02d:%02d" % (hours, minutes, seconds)
+
+
+def quantile_exc(ser: Iterable[float], quantile: float) -> float:
+    """Python implemenation of Excels PERCENTILE.EXC. Refer to
+    Microsoft documentation for more details.
+
+    Args:
+        ser: Series of values to calculate quantile from
+        quantile: Quantile to calculate
+
+    Returns:
+        float: Value for specified quantile
+    """
+
+    ser_sorted = np.sort(np.array(ser))
+    rank = quantile * (len(ser) + 1) - 1
+    assert rank > 0, "quantile is too small"
+    rank_l = int(rank)
+    return ser_sorted[rank_l] + (ser_sorted[rank_l + 1] - ser_sorted[rank_l]) * (
+        rank - rank_l
+    )
+
+
+def percentile_rank_exc(x: Iterable[float], number: int) -> float:
+    """Python implementation of excel function PERCENTRANK.EXC.
+    Refer to Microsoft documentation for more details.
+
+    Args:
+        x: Iterable of floats to cacl precent rank from
+        number: Number to use to calc percent rank from array
+
+    Returns:
+        float: Percent rank excl covered
+    """
+    y = [(sum(pd.Series(x) < i) + 1) / (len(x) + 1) for i in x]
+    f = interpolate.interp1d(x, y)
+    return float(f(number))
+
+
+def large(array: Iterable[int], k: int) -> float:
+    """Python implementation of excels LARGE function, returns kth largest value
+    in array, if empty array or asking for kth value which is <= 0 or greater than
+    number of data points return 0.
+
+    Args:
+        array (Iterable[int]): Iterable with numbers to find kth largest
+        k (int): Kth largest value to find
+
+    Returns:
+        float: Kth largest value or 0
+    """
+
+    if k > len(array) or k <= 0:
+        return 0
+    array.sort()
+    try:
+        return array[len(array) - k]
+    except IndexError:
+        return 0
+
+
+def invert_rank(org_ranks: Iterable[int]) -> list[int]:
+    """Inverts the rank of a list of integers, i.e. 1 becomes the largest."""
+    num_distinct_ranks = max(org_ranks)
+    inverted = num_distinct_ranks + 1 - rankdata(org_ranks, method="dense")
+    return inverted.astype(int).tolist()
+
+
+def order_by_schema(df: pd.DataFrame, schema: DataFrameModel) -> pd.DataFrame:
+    """Order the dataframe by the provided schema."""
+
+    schema_order = list(schema.to_schema().columns.keys())
+    return df[schema_order]
+
+
+def get_scenario(key: tuple[str, Item]) -> str:
+    """Get the scenario from the result key."""
+    scenario = key[0]
+    return scenario.split(".csv")[0]
+
+
+def get_scenario_from_str(scenario: str) -> str:
+    """Get the scenario from the result key."""
+    pattern = r"\d\d"
+    match = re.search(pattern, scenario)
+    if match:
+        return match.group(0)
+    return "actual"
+
+
+def determine_run_pcnt(file_name: str) -> str:
+    """Given the file name determine what inventory scenario it is for.
+    E.g acutal_65pct.csv -> 65pct.
+    """
+
+    match = re.search(r"_([\d]+)pct\.csv", file_name)
+    if match:
+        return match.group(1)
+    return "actual"
+
+
+def calc_bal_metric(run_pct: str, result: Analysis) -> pd.DataFrame:
+    """Calculate the bal metric for the given run pct and result."""
+
+    current_bal_df = result.balance_metric.copy()
+    current_bal_df = current_bal_df.reset_index()
+    current_bal_df = current_bal_df[["objective", "balanceMetric"]]
+    current_bal_df[BalMetricsDashboard.item] = result.item
+    current_bal_df[BalMetricsDashboard.run_pct] = run_pct
+    return current_bal_df
+
+
+def map_warehouse_province(
+    province_df: pat.DataFrame[ProvinceLookupDF],
+    duals_df: pd.DataFrame,
+) -> pd.Series:
+    """Map the warehouse id to province."""
+    province_lookup = province_df.set_index(
+        ProvinceLookupDF.warehouse_id,
+    ).to_dict()[ProvinceLookupDF.province]
+
+    # Split out the warehouse id and map to province
+    return (
+        duals_df[DualsByWharehouseDF.warehouse_id]
+        .str.split(",", expand=True)[0]
+        .map(province_lookup)
+    )
+
+
+def frac_disaster_covered(
+    people_served: int,
+    disaster_sizes: Iterable[int],
+) -> float:
+    """Calculate the fraction of the disaster covered by the current inventory."""
+
+    disaster_sizes = np.sort(disaster_sizes)
+
+    if people_served < min(disaster_sizes):
+        return 0
+    if people_served > max(disaster_sizes):
+        return 1
+    return round(percentile_rank_exc(disaster_sizes, people_served), 2)
+
+
+def get_actual_inventory(dataset: Dataset) -> dict[str, int]:
+    """Get the recommended inventory levels for each item at different percentiles."""
+
+    inv_results = {}
+    actual_inv = {item.id: 0 for item in dataset.items}
+    actual = dataset.inventory_scenarios["actual.csv"]
+    for key, value in actual.items():
+        item = key[1].id
+        actual_inv[item] += value
+    scenario_formatted = get_scenario_from_str("actual.csv")
+    inv_results[scenario_formatted] = actual_inv
+    return inv_results
+
+
+def enough_stock(
+    item: str,
+    scenario: float,
+    exisiting_stock: dict[str, int],
+    recommended_level_stock: dict[float, int],
+) -> bool:
+    """Determine if there is enough stock for the given item and scenario."""
+    return exisiting_stock[item] >= recommended_level_stock[scenario][item]
+
+
+def calc_increase_stock_pct(
+    item: str,
+    scenario: float,
+    current_levels: dict[str, int],
+    recommended_levels: dict[float, int],
+) -> float:
+    """Calculate the increase stock percentage for the given item and scenario."""
+
+    recommended = recommended_levels[scenario][item]
+    current = current_levels[item]
+
+    if current == 0:
+        return np.inf
+    pct_change = round((recommended - current) / current, 2)
+    return max(pct_change, 0) * 100
+
+
+def create_scenario_combo(
+    user_location: Iterable[str],
+    items: Iterable[str],
+) -> Iterable[tuple[str, str, str, bool, int]]:
+    """Create the scenario combinations.
+
+    The combinations are of all the user locations, items, disaster coverage levels,
+    move from and rank options. The disaster coverage levels the move from and
+    rank options are fixed.
+
+    Args:
+        user_location: The user locations.
+        items: The items.
+
+    Returns:
+        The scenario combinations.
+
+    """
+
+    percentage_disasters_cover = [50, 55, 60, 65, 70, 75, 80, 85, 90, 95]
+    percentage_disasters_cover = [str(i) for i in percentage_disasters_cover]
+    move_from = [False, True]
+    rank = list(range(1, 5))
+
+    return product(user_location, items, percentage_disasters_cover, move_from, rank)
diff --git a/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_value_objects.py b/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_value_objects.py
new file mode 100644
index 0000000..d8b64af
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/dashboard_utils/dashboard_value_objects.py
@@ -0,0 +1,322 @@
+import pandera as pa
+from pandera import DataFrameModel, Field
+from pandera.typing import Series
+from pydantic import BaseModel
+from typing_extensions import Self
+
+from src.analysis import SolverObjective
+
+
+class ExistingStockInfo(BaseModel):
+    """Existing stock information.
+
+    Attributes:
+        recommended_stock: The recommended stock.
+        initial_stock: The initial stock.
+        ppl_served_per_item: The people served per item.
+        scenario_disaster_size: The scenario disaster size.
+        frac_disaster_covered: The fraction of the disaster covered.
+        increase_stock_pct: The increase stock percentage.
+        enough_stock: Whether there is enough stock.
+        ppl_served_exisiting: The people served by the existing stock.
+
+    """
+
+    recommended_stock: dict[float, dict[str, int]]
+    initial_stock: dict[str, int]
+    ppl_served_per_item: dict[str, float]
+    scenario_disaster_size: dict[float, float]
+    frac_disaster_covered: dict[str, float]
+    increase_stock_pct: dict[tuple[str, float], float]
+    enough_stock: dict[tuple[str, float], bool]
+    ppl_served_exisiting: dict[str, int]
+
+
+class DualsByWharehouseDF(DataFrameModel):
+    """Duals by wharehouse dataframe value object.
+
+    Attributes:
+        item_type: The item type
+        objCostType: The objective cost type (Time or Cost)
+        rawDual: The raw dual
+        warehouseID: The warehouse ID
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    item_type: Series[str]
+    objective: Series[int]
+    raw_dual: Series[float] = Field(coerce=True)
+    warehouse_id: Series[str]
+
+    @pa.check("objective")
+    @classmethod
+    def check_objective(cls: type[Self], objectives: Series[int]) -> Series[bool]:
+        """Check that the objective is either 0 or 1."""
+
+        valid_values = list(SolverObjective)
+        return objectives.isin(valid_values)
+
+
+class BalMetricsDashboard(DataFrameModel):
+    """A dataframe model for the balance metrics dashboard.
+
+    Attributes:
+        item: The item
+        run_pct: The run percentage
+        cost: The cost savings
+        time: The time savings
+        grand_total: The grand total
+        country: The country the results are for.
+
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    item: Series[str]
+    run_pct: Series[str]
+    cost: Series[float] = Field(ge=0)
+    time: Series[float] = Field(ge=0)
+    grand_total: Series[float] = Field(ge=0)
+    country: Series[str]
+
+
+class ProvinceAssessDF(DataFrameModel):
+    """A dataframe model for the province assessment dashboard file.
+
+    Attributes:
+        province: The province.
+        cost: The cost savings
+        time: The time savings
+        time_hms: The time savings in hours, minutes, seconds
+        country: The country the results are for.
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    province: Series[str]
+    cost: Series[float]
+    time: Series[float]
+    time_hms: Series[str]
+    country: Series[str]
+
+
+class ItemProvinceAssessDF(DataFrameModel):
+    """A dataframe model for the item province assessment dashboard file."""
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    province: Series[str]
+    cost: Series[float]
+    time: Series[float]
+    time_hms: Series[str]
+    item_type: Series[str]
+    country: Series[str]
+
+
+class ProvinceLookupDF(DataFrameModel):
+    """A dataframe model for the province lookup file.
+
+    Attributes:
+        province: The province.
+        warehouse_id: The warehouse ID.
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    province: Series[str]
+    warehouse_id: Series[str]
+
+
+class ExisitingStockAssessDF(DataFrameModel):
+    """A dataframe model for the existing stock assessment dashboard file.
+
+    Attributes:
+        item: The item type.
+        ppl_served_per_item: The people served per item.
+        frac_disaster_covered: The fraction of the disaster covered by exisiting stock.
+        ppl_served_exisiting: The people served by the existing stock.
+        ppl_affected: The number of people affected by disasters for current scenario .
+        enough_stock: Whether there is enough stock.
+        recommended_stock: The recommended stock for the selected scenario.
+        increase_stock_pct: The percentage to increase stock by to meet recommended
+        level.
+        scenario: The scenario, i.e 50th percentile.
+        exisiting_inventory: The existing inventory levels.
+        country: The country the results are for.
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    item: Series[str]
+    ppl_served_per_item: Series[float]
+    frac_disaster_covered: Series[float]
+    ppl_served_exisiting: Series[int]
+    ppl_affected: Series[float]
+    enough_stock: Series[bool]
+    recommended_stock: Series[int]
+    increase_stock_pct: Series[float]
+    scenario: Series[float]
+    exisiting_inventory: Series[int]
+    country: Series[str]
+
+
+class SingleWarehouseMoveDF(DataFrameModel):
+    """A dataframe model for the single warehouse move dataframe.
+    This value object will also have percentile columns these
+    will changed based on the percentiles selected for analysis
+    hence the strict=True is excluded.
+
+    Attributes:
+        location: The location.
+        ideal_stock: The ideal stock level based on user percentage.
+        extra_stock: The extra stock level for user percentage.
+        required_stock: The required stock level for user percentage.
+        rank_extra_stock: The rank of the extra stock compared with other warehouses.
+            I.e a rank of 1 indicates this warehouse has the most extra stock for
+            the item.
+        rank_required_stock: The rank of the required stock compared with other
+            warehouses. I.e a rank of 1 indicates this warehouse needs the most extra
+            stock for the item.
+        actual_stock_lvl: The actual stock level at the warehouse.
+        item: The item.
+        country: The country the results are for.
+
+    """
+
+    location: Series[str]
+    ideal_stock: Series[float]
+    extra_stock: Series[float]
+    required_stock: Series[float]
+    rank_extra_stock: Series[int]
+    rank_required_stock: Series[int]
+    actual_stock_lvl: Series[float]
+    item: Series[str]
+    country: Series[str]
+
+
+class OptimalStockDF(DataFrameModel):
+    """A dataframe model for the optimal stock dataframe.
+
+    Attributes:
+        item: The item.
+        warehouse_id: The warehouse ID.
+        location: The province (admin1) the warehouse is located in.
+        percentile: The percentile.
+        optimal_stock: The optimal stock level for that item and warehouse at the
+            selected percentile.
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    item: Series[str]
+    warehouse_id: Series[str]
+    location: Series[str]
+    percentile: Series[str]
+    optimal_stock: Series[float]
+
+
+class Admin1ActualInvDF(DataFrameModel):
+    """A dataframe model for the admin1 actual inventory dataframe.
+
+    This is stock which is actually in the facilities for an admin area.
+
+    Attributes:
+        location: The admin1 location.
+        item: The item.
+        actual_stock_lvl: The actual stock level for the admin.
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    location: Series[str]
+    item: Series[str]
+    actual_stock_lvl: Series[float] = Field(coerce=True)
+
+
+class ReallocationOptionsDF(DataFrameModel):
+    """A dataframe model for the reallocation options dataframe.
+
+    Attributes:
+        location: The location to ether move stock from or to.
+        actual_stock_lvl: The actual stock level at the location.
+        extra_or_needed_stock: The extra or needed stock level at the location.
+        cost: The cost savings from moving stock to/from the location.
+        time: The time savings from moving stock to/from the location. Formatted as
+            HH:MM:SS.
+        move_from: A string indicating whether the item is needed or extra. If the value
+            is needed this indicates the stock is being moved from the location to
+            the user_location. If the value is extra the opposite is true.
+        user_location: The other location the stock is moving to/from.
+        scenario: The disaster scenario. i.e 50th percentile.
+        item: The item to move.
+        country: The country the results are for.
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    location: Series[str]
+    actual_stock_lvl: Series[float]
+    extra_or_needed_stock: Series[float]
+    cost: Series[float]
+    time: Series[str]
+    move_from: Series[str] = Field(isin=["Needed", "Extra"])
+    user_location: Series[str]
+    scenario: Series[int]
+    item: Series[str]
+    country: Series[str]
+
+
+class DisasterTotals(DataFrameModel):
+    """A dataframe model for the disaster totals dataframe.
+
+    Attributes:
+        disaster_id: The disaster ID.
+        total_affected: The total number of people affected.
+        country: The country the disaster occurred in.
+
+    """
+
+    class Config:
+        """Pandera configuration."""
+
+        strict = True
+
+    disaster_id: Series[str]
+    total_affected: Series[float] = Field(coerce=True, ge=1)
+    country: Series[str]
diff --git a/optimization202/ESUPS_case_study/src/dashboard_utils/dauls_utils.py b/optimization202/ESUPS_case_study/src/dashboard_utils/dauls_utils.py
new file mode 100644
index 0000000..17625bc
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/dashboard_utils/dauls_utils.py
@@ -0,0 +1,135 @@
+"""Functions relating to calculating duals by warehouse for the dashboard files."""
+
+import pandas as pd
+import pandera.typing as pat
+
+from src.analysis import Analysis, SolverObjective
+from src.dashboard_utils.dashboard_value_objects import DualsByWharehouseDF
+from src.solving import AllocationStrategy
+
+OBJECTIVE_STR_MAP = {
+    SolverObjective.Cost: "cost",
+    SolverObjective.Time: "time",
+    SolverObjective.Distance: "distance",
+}
+
+
+def _calc_normalized_shadow_price(row: pd.Series) -> float:
+    """Calculate the normalized shadow price for a row in the duals_by_warehouse df."""
+    if row[DualsByWharehouseDF.objective] == OBJECTIVE_STR_MAP[SolverObjective.Cost]:
+        return row[DualsByWharehouseDF.raw_dual] - row["MeanCost"]
+
+    return (row[DualsByWharehouseDF.raw_dual] - row["MeanTime"]) / 24
+
+
+def create_mean_duals(duals_df: pat.DataFrame[DualsByWharehouseDF]) -> pd.DataFrame:
+    """Create a dataframe of the mean duals for each item type and objective.
+
+    Args:
+        duals_df: The dataframe containing the duals by warehouse
+
+    Returns:
+        A dataframe of the mean duals for each item type and objective.
+
+    """
+    mean_raw_duals = (
+        duals_df.groupby(
+            [DualsByWharehouseDF.item_type, DualsByWharehouseDF.objective],
+        )[DualsByWharehouseDF.raw_dual]
+        .agg("mean")
+        .reset_index()
+    )
+    mean_raw_duals = mean_raw_duals.pivot_table(
+        index=DualsByWharehouseDF.item_type,
+        columns=DualsByWharehouseDF.objective,
+        values=DualsByWharehouseDF.raw_dual,
+    )
+    mean_raw_duals = mean_raw_duals.rename(
+        columns={SolverObjective.Cost: "MeanCost", SolverObjective.Time: "MeanTime"},
+    )
+    return mean_raw_duals.reset_index()
+
+
+def add_normalized_shadow_price(
+    duals_df: pat.DataFrame[DualsByWharehouseDF],
+    mean_duals_df: pd.DataFrame,
+) -> pd.DataFrame:
+    """Add the normalized shadow price to the duals dataframe.
+
+    Args:
+        duals_df: The dataframe containing the duals by warehouse
+        mean_duals_df: The dataframe containing the mean duals for each item type and
+        objective.
+
+    Returns:
+        The duals dataframe with the normalized shadow price added.
+
+    """
+
+    duals_df_copy = duals_df.copy()
+    duals_df_copy = duals_df_copy.merge(
+        mean_duals_df,
+        on=DualsByWharehouseDF.item_type,
+    )
+    duals_df_copy[DualsByWharehouseDF.objective] = duals_df_copy[
+        DualsByWharehouseDF.objective
+    ].map(OBJECTIVE_STR_MAP)
+
+    duals_df_copy["NormalizedShadowPrice"] = duals_df_copy.apply(
+        _calc_normalized_shadow_price,
+        axis=1,
+    )
+
+    return duals_df_copy
+
+
+def calc_duals_by_warehouse(result: Analysis) -> pat.DataFrame[DualsByWharehouseDF]:
+    """Creates a `DualsByWharehouseDF` dataframe for the time and cost objectives
+    of a given `Analysis` result.
+
+    Args:
+        result: The analysis result
+
+    Returns:
+        A dataframe of raw duals by warehouse for the time and cost objectives for
+        the item and scenario.
+
+    """
+    item = result.item.id
+    solution = result.solutions
+
+    cost_solution = solution[
+        (SolverObjective.Cost, AllocationStrategy.MinimizeFixedInventory)
+    ]
+    time_solution = solution[
+        (SolverObjective.Time, AllocationStrategy.MinimizeFixedInventory)
+    ]
+    cost_duals = cost_solution.duals_inventory_exc_dummy_unadjusted
+    time_duals = time_solution.duals_inventory_exc_dummy_unadjusted
+
+    # Get the depots as str rather than `Depot` object
+    cost_depot_ids = [depot.id for depot in cost_duals]
+    time_depot_ids = [depot.id for depot in time_duals]
+
+    # Get duals for cost and time objectives
+    cost_duals_df = pd.DataFrame(
+        dict(
+            item_type=item,
+            objective=SolverObjective.Cost,
+            raw_dual=cost_duals.values(),
+            warehouse_id=cost_depot_ids,
+        ),
+    )
+    time_duals_df = pd.DataFrame(
+        dict(
+            item_type=item,
+            objective=SolverObjective.Time,
+            raw_dual=time_duals.values(),
+            warehouse_id=time_depot_ids,
+        ),
+    )
+
+    duals_df = pd.concat([cost_duals_df, time_duals_df], ignore_index=True)
+    duals_df = duals_df.reset_index(drop=True)
+
+    return DualsByWharehouseDF.validate(duals_df)
diff --git a/optimization202/ESUPS_case_study/src/dashboard_utils/reallocations_utils.py b/optimization202/ESUPS_case_study/src/dashboard_utils/reallocations_utils.py
new file mode 100644
index 0000000..804002a
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/dashboard_utils/reallocations_utils.py
@@ -0,0 +1,319 @@
+"""Functions relating to the reallocation dashboard files."""
+
+from collections.abc import Generator
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from pandera.typing import DataFrame
+from tqdm import tqdm
+
+from src.dashboard_utils.dashboard_utils import invert_rank, large, rankdata
+from src.dashboard_utils.dashboard_value_objects import (
+    Admin1ActualInvDF,
+    ItemProvinceAssessDF,
+    OptimalStockDF,
+    ReallocationOptionsDF,
+    SingleWarehouseMoveDF,
+)
+
+
+def create_admin1_act_inv(
+    starting_inv_path: Path,
+    admin1_lookup: dict[str, str],
+) -> DataFrame[Admin1ActualInvDF]:
+    """Create the admin1 actual inventory dataframe.
+
+    Args:
+        starting_inv_path: The path to the starting inventory file.
+        admin1_lookup: A dictionary of the warehouse id and the admin 1.
+
+    Returns:
+        The admin1 actual inventory dataframe.
+
+    """
+    starting_inventory = pd.read_csv(starting_inv_path)
+
+    starting_inventory[Admin1ActualInvDF.location] = starting_inventory[
+        "gglAddress"
+    ].apply(lambda x: admin1_lookup[x.split(",")[0]])
+
+    starting_inventory = (
+        starting_inventory.groupby([Admin1ActualInvDF.location, "ItemName"])["Total"]
+        .sum()
+        .reset_index()
+    )
+    starting_inventory = starting_inventory.rename(
+        columns={
+            "ItemName": Admin1ActualInvDF.item,
+            "Total": Admin1ActualInvDF.actual_stock_lvl,
+        },
+    )
+
+    _ = Admin1ActualInvDF.validate(starting_inventory)
+    return starting_inventory
+
+
+def create_starting_stock_lkup(
+    all_inv_df: DataFrame[OptimalStockDF],
+) -> dict[tuple[str, str], int]:
+    """For each location and item in `all_inv_df` determine the actual inventory levels.
+
+    Args:
+        all_inv_df: Dataframe which has actual stock levels for each item and
+        location.
+
+    Returns:
+        A dictionary with the location and item as the key and the actual stock
+        level as the value.
+
+    """
+
+    actual_inv = all_inv_df.copy()
+    actual_inv = actual_inv.loc[actual_inv[OptimalStockDF.percentile] == "actual"]
+    inventory_lkup = actual_inv.groupby([OptimalStockDF.location, OptimalStockDF.item])[
+        OptimalStockDF.optimal_stock
+    ].unique()
+
+    same_lvls = inventory_lkup.apply(len)
+    assert not all(
+        same_lvls > 1,
+    ), "Some location item combinations have different starting inventories"
+
+    return inventory_lkup.apply(lambda x: x[0]).to_dict()
+
+
+def find_current_stocks(
+    item: str,
+    location: str,
+    starting_inventory: dict[tuple[str, str], int],
+) -> pd.Series:
+    """Find the current stock levels for an item in each area.
+
+    Args:
+        item: The item to find stocks for
+        location: The location to find stocks for
+        starting_inventory: A dictionary with the location and item as the key and
+        the actual stock level as the value.
+
+    Returns:
+        Series: Index is location and then value is stock value
+    """
+
+    actual_level = starting_inventory.get((location, item), None)
+
+    if actual_level is None:
+        err_msg = f"Location {location} and item {item} not found in starting inventory"
+        raise ValueError(err_msg)
+
+    return actual_level
+
+
+def single_warehouse_move_to(
+    item: str,
+    user_percentage: int,
+    item_percentile_lvl_df: pd.DataFrame,
+    country: str,
+) -> DataFrame[SingleWarehouseMoveDF]:
+    """For a given item and user percentage, for each location rank the amount
+    of extra/needed stock and the amount of extra/needed stock.
+
+    Args:
+        item: The item of interest
+        user_percentage: Percentage of diasters to cover
+        item_percentile_lvl_df: Dataframe with the optimal/actual stock levels for
+        each item and location.
+        country: The country the results are for.
+
+    Returns:
+        DataFrame: Returns dataframe with all this information as columns
+    """
+
+    percentile_stock_opt_levels = item_percentile_lvl_df.loc[
+        item_percentile_lvl_df[SingleWarehouseMoveDF.item] == item
+    ].copy()
+
+    percentile_stock_opt_levels = percentile_stock_opt_levels.set_index(
+        SingleWarehouseMoveDF.location,
+    )
+
+    # Find the ideal stock levels based on the user percentage
+    percentile_stock_opt_levels[
+        SingleWarehouseMoveDF.ideal_stock
+    ] = percentile_stock_opt_levels[user_percentage]
+
+    # Calc if there is extra or required stock
+    percentile_stock_opt_levels[SingleWarehouseMoveDF.extra_stock] = np.maximum(
+        percentile_stock_opt_levels[SingleWarehouseMoveDF.actual_stock_lvl]
+        - percentile_stock_opt_levels[SingleWarehouseMoveDF.ideal_stock],
+        0,
+    )
+    percentile_stock_opt_levels[SingleWarehouseMoveDF.required_stock] = np.maximum(
+        percentile_stock_opt_levels[SingleWarehouseMoveDF.ideal_stock]
+        - percentile_stock_opt_levels[SingleWarehouseMoveDF.actual_stock_lvl],
+        0,
+    )
+
+    # Rank the extra/needed stock
+    percentile_stock_opt_levels[SingleWarehouseMoveDF.rank_extra_stock] = rankdata(
+        percentile_stock_opt_levels[SingleWarehouseMoveDF.extra_stock],
+        method="dense",
+    )
+    percentile_stock_opt_levels[SingleWarehouseMoveDF.rank_extra_stock] = invert_rank(
+        list(percentile_stock_opt_levels[SingleWarehouseMoveDF.rank_extra_stock]),
+    )
+    percentile_stock_opt_levels[SingleWarehouseMoveDF.rank_required_stock] = rankdata(
+        percentile_stock_opt_levels[SingleWarehouseMoveDF.required_stock],
+        method="dense",
+    )
+    percentile_stock_opt_levels[
+        SingleWarehouseMoveDF.rank_required_stock
+    ] = invert_rank(
+        list(percentile_stock_opt_levels[SingleWarehouseMoveDF.rank_required_stock]),
+    )
+    percentile_stock_opt_levels = percentile_stock_opt_levels.reset_index()
+    percentile_stock_opt_levels[SingleWarehouseMoveDF.country] = country
+
+    _ = SingleWarehouseMoveDF.validate(percentile_stock_opt_levels)
+    return percentile_stock_opt_levels
+
+
+def move_to_from_location(k: int, extra: bool, item_stock_df: DataFrame) -> pd.Series:
+    """For a given rank of extra/needed stock find the warehouse log cluster and the
+    amount of stock for that rank.
+
+    I.e if k = 1 and extra = True then find the warehouse log cluster
+    with the highest extra stock and the amount of extra stock based off item_stock_df.
+
+
+    Args:
+        k: The rank of the extra/needed stock i.e calculate for highest ranked
+        extra: Determines whether calculating for extra items or for needed items
+        item_stock_df: The dataframe containing the extra/needed stock for an item
+
+    Returns:
+        Series: Empty series if no extra/needed stock, otherwise returns the warehouse
+        log cluster and stock levels
+    """
+
+    # Determine if calculating for extra or needed stock
+    extra_or_needed = (
+        SingleWarehouseMoveDF.extra_stock
+        if extra
+        else SingleWarehouseMoveDF.required_stock
+    )
+
+    # Find the kth largest extra/needed stock amount
+    kth_largest = large(list(item_stock_df[extra_or_needed]), k)
+
+    if kth_largest == 0:
+        return pd.Series(dtype="float64")
+
+    # If there is extra/needed stock return the warehouse log cluster and the amount
+    # of stock
+    # In case of more than one with same value will take first index one
+    move_idx = list(item_stock_df[extra_or_needed]).index(kth_largest)
+    return item_stock_df.loc[move_idx][
+        [
+            SingleWarehouseMoveDF.location,
+            SingleWarehouseMoveDF.actual_stock_lvl,
+            extra_or_needed,
+        ]
+    ]
+
+
+def reallocation_option_loop(
+    scenario_combos: Generator[tuple[str, str, str, bool, int]],
+    num_scenarios: int,
+    item_percentile_lvl_df: pd.DataFrame,
+    geo_wh_stock: DataFrame[ItemProvinceAssessDF],
+    country: str,
+) -> tuple[DataFrame[ReallocationOptionsDF], DataFrame[SingleWarehouseMoveDF]]:
+    """Create the reallocation and single warehouse move from dataframes.
+
+
+    Loops over all combinations and calaculates the reallocation options and single
+    warehouse move from dataframes.
+
+    Args:
+        scenario_combos: The scenario combinations.
+        num_scenarios: The number of scenarios.
+        item_percentile_lvl_df: The item percentile level dataframe.
+        geo_wh_stock: The geo warehouse stock dataframe.
+        country: The country the results are for.
+
+    Returns:
+        A tuple containing the reallocation options and single warehouse move from
+        dataframes.
+
+    """
+    reallocation_options = pd.DataFrame()
+    single_warehouse_move_from = pd.DataFrame()
+    geo_wh_stock = geo_wh_stock.drop(columns=[ItemProvinceAssessDF.country])
+
+    for location, item, disaster_coverage, move, rank in tqdm(
+        scenario_combos,
+        total=num_scenarios,
+        desc="Progress",
+    ):
+        # Determine optimum stock levels for the item at the disaster coverage level at
+        # each warehouse log cluster
+        percentile_stock_opt_levels = single_warehouse_move_to(
+            item,
+            disaster_coverage,
+            item_percentile_lvl_df=item_percentile_lvl_df,
+            country=country,
+        )
+
+        # Determine the location to move stock to/from based for given rank
+        other_loc_and_stock = move_to_from_location(
+            rank,
+            move,
+            percentile_stock_opt_levels,
+        )
+
+        # Not all combinations have reallocation options this is mainly the case
+        # when we want to move extra stock as there often isn't extra stock in all or
+        # even any locations
+        if other_loc_and_stock.empty:
+            continue
+
+        other_location = other_loc_and_stock[OptimalStockDF.location]
+
+        # If we are moving stock from this location to the other location
+        if move:
+            time_cost_savings = (
+                geo_wh_stock.loc[other_location, item]
+                - geo_wh_stock.loc[location, item]
+            )
+
+        # Else we are moving stock from the other location to this location
+        else:
+            time_cost_savings = (
+                geo_wh_stock.loc[location, item]
+                - geo_wh_stock.loc[other_location, item]
+            )
+
+        current_reallocation = pd.DataFrame(
+            [pd.concat([other_loc_and_stock, time_cost_savings])],
+        )
+        current_reallocation = current_reallocation.rename(
+            columns={
+                SingleWarehouseMoveDF.extra_stock: ReallocationOptionsDF.extra_or_needed_stock,  # noqa: E501
+                SingleWarehouseMoveDF.required_stock: ReallocationOptionsDF.extra_or_needed_stock,  # noqa: E501
+            },
+        )
+
+        current_reallocation[ReallocationOptionsDF.move_from] = move
+        current_reallocation[ReallocationOptionsDF.user_location] = location
+        current_reallocation[ReallocationOptionsDF.scenario] = disaster_coverage
+        current_reallocation[ReallocationOptionsDF.item] = item
+        percentile_stock_opt_levels[SingleWarehouseMoveDF.item] = item
+
+        reallocation_options = pd.concat([reallocation_options, current_reallocation])
+        single_warehouse_move_from = pd.concat(
+            [single_warehouse_move_from, percentile_stock_opt_levels],
+            ignore_index=True,
+        )
+
+    return reallocation_options, single_warehouse_move_from
diff --git a/optimization202/ESUPS_case_study/src/data.py b/optimization202/ESUPS_case_study/src/data.py
new file mode 100644
index 0000000..b18e19b
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/data.py
@@ -0,0 +1,214 @@
+import functools
+from dataclasses import dataclass, field
+from typing import Callable, Tuple
+
+
+@dataclass(frozen=True)
+class Country:
+    id: str = field(hash=True)
+    continent: str = field(repr=False)
+
+
+@dataclass(frozen=True)
+class Location:
+    id: str = field(hash=True)
+    address: str = field(repr=False)
+    country: Country = field(repr=False)
+    latitude: float = field(repr=False)
+    longitude: float = field(repr=False)
+
+
+@dataclass(frozen=True)
+class DisasterType:
+    id: str
+
+
+@dataclass(frozen=True)
+class DisasterImpact:
+    id: str
+    disaster: "Disaster" = field(repr=False)
+    location: "DisasterLocation" = field(repr=False)
+    sub_location_nr: int = field(repr=False)
+    total_affected: int = field(repr=False)
+
+    def __repr__(self):
+        return self.id
+
+
+@dataclass(frozen=True)
+class DisasterLocation(Location):
+    def __repr__(self):
+        return self.id
+
+
+@dataclass(frozen=True)
+class Disaster:
+    id: str
+    type: DisasterType = field(repr=False)
+    day: int = field(repr=False)
+    month: int = field(repr=False)
+    year: int = field(repr=False)
+    impacted_locations: list[DisasterImpact] = field(hash=False, repr=False)
+
+
+@dataclass(frozen=True)
+class Depot(Location):
+    pass
+
+
+@dataclass(frozen=True)
+class Item:
+    id: str = field(hash=True)
+    weight: float = field(repr=False)  # Metric tons
+    volume: float = field(repr=False)  # Cubic metres
+
+
+@dataclass(frozen=True)
+class TransportMode:
+    id: str = field(hash=True)
+    distance_method: str
+    big_m_cost_elim: float
+    max_driving_time_cut_above_hrs: float
+
+
+@dataclass(frozen=True)
+class DistanceInfo:
+    distance: float  # Kilometres
+    time: float  # Hours
+    cost_per_ton: float  # USD
+
+
+DistanceMatrix = dict[Tuple[Location, Location, TransportMode], DistanceInfo]
+
+@dataclass(frozen=True)
+class Dataset:
+    depots: list[Depot]
+    disasters: list[Disaster]
+    disaster_locations: list[DisasterLocation]
+    probabilities: dict[Disaster, float]
+    items: list[Item]
+    transport_modes: list[TransportMode]
+    inventory: dict[Tuple[Depot, Item], int]
+    inventory_scenarios: dict[str, dict[Tuple[Depot, Item], int]]
+    distance: DistanceMatrix
+    people_affected: dict[Tuple[DisasterImpact, Item], float]
+    persons_per_item_general: dict[Tuple[DisasterImpact, Item], float]
+    persons_per_item_monthly: dict[Tuple[DisasterImpact, Item], float]
+    disaster_affected_totals: dict[str, int]
+
+    _zero_demand_threshold = 1e6
+
+    def take_disaster_subset(self, predicate: Callable[[Disaster], bool]) -> "Dataset":
+        """
+        Generate a smaller dataset by only selecting a subset of the disasters with corresponding data
+        """
+        disasters = list(filter(predicate, self.disasters))
+
+        if len(disasters) == len(self.disasters):
+            return self
+
+        total_probability = sum(self.probabilities[disaster] for disaster in disasters)
+        probabilities = {
+            disaster: self.probabilities[disaster] / total_probability
+            for disaster in disasters
+        }
+        locations = [
+            impact.location
+            for disaster in disasters
+            for impact in disaster.impacted_locations
+        ]
+        distance = {
+            (source, destination, mode): cell
+            for (source, destination, mode), cell in self.distance.items()
+            if destination in locations
+        }
+        people_affected = {
+            (location, item): value
+            for (location, item), value in self.people_affected.items()
+            if location in locations
+        }
+        persons_per_item_general = {
+            (location, item): value
+            for (location, item), value in self.persons_per_item_general.items()
+            if location in locations
+        }
+        persons_per_item_monthly = {
+            (location, item): value
+            for (location, item), value in self.persons_per_item_monthly.items()
+            if location in locations
+        }
+        return Dataset(
+            self.depots,
+            disasters,
+            locations,
+            probabilities,
+            self.items,
+            self.transport_modes,
+            self.inventory,
+            self.inventory_scenarios,
+            distance,
+            people_affected,
+            persons_per_item_general,
+            persons_per_item_monthly,
+            self.disaster_affected_totals,
+        )
+
+    def take_inventory_scenario(self, filename: str):
+        if filename not in self.inventory_scenarios:
+            raise RuntimeError("Inventory scenario not found")
+        inventory = self.inventory_scenarios[filename]
+        if inventory == self.inventory:
+            return self
+        return Dataset(
+            self.depots,
+            self.disasters,
+            self.disaster_locations,
+            self.probabilities,
+            self.items,
+            self.transport_modes,
+            inventory,
+            self.inventory_scenarios,
+            self.distance,
+            self.people_affected,
+            self.persons_per_item_general,
+            self.persons_per_item_monthly,
+            self.disaster_affected_totals,
+        )
+
+    @functools.cached_property
+    def general_demand(self) -> dict[Tuple[DisasterImpact, Item], float]:
+        general_demand = {
+            (location, item): self._calc_items_needed(
+                self.people_affected[location, item],
+                self.persons_per_item_general[location, item],
+            )
+            for disaster in self.disasters
+            for location in disaster.impacted_locations
+            for item in self.items
+        }
+
+        return {key: value for key, value in general_demand.items() if value > 1e-1}
+
+    @functools.cached_property
+    def monthly_demand(self) -> dict[Tuple[DisasterImpact, Item], float]:
+        monthly_demand = {
+            (location, item): self._calc_items_needed(
+                self.people_affected[location, item],
+                self.persons_per_item_monthly[location, item],
+            )
+            for disaster in self.disasters
+            for location in disaster.impacted_locations
+            for item in self.items
+        }
+
+        return {key: value for key, value in monthly_demand.items() if value > 1e-3}
+
+    def _calc_items_needed(
+        self,
+        people_affected: float,
+        beta: float,
+    ):
+        if beta == 0 or beta >= self._zero_demand_threshold:
+            return 0
+        else:
+            return people_affected / beta
diff --git a/optimization202/ESUPS_case_study/src/lookup.py b/optimization202/ESUPS_case_study/src/lookup.py
new file mode 100644
index 0000000..2317220
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/lookup.py
@@ -0,0 +1,91 @@
+import pandas as pd
+
+"""
+Subtable for lookups. This implementation uses an internal Pandas dataframe. Ideas to improve:
+- When the number of rows is small, just iterating may be faster than using masks
+- When the table is accessed from a loop involving multiple dimensions, we may benefit from caching combined masks
+"""
+
+
+class LookupSubtablePandas:
+    def __init__(self, df: pd.DataFrame, defaults: dict[str, any]):
+        self._data = df
+        self._lruValues = {}
+        self._lruData = {}
+        self._defaults = defaults
+
+    def lookup(self, filter: dict[str, any]):
+        result = self._data
+        prefix_match = True
+        for key, _ in self._defaults.items():
+            value = filter[key]
+            if prefix_match and self._lruValues.get(key) == value:
+                result = self._lruData[key]
+            else:
+                prefix_match = False
+                self._lruData[key] = result = result[
+                    result[key].isin([value, self._defaults[key]])
+                ]
+                self._lruValues[key] = value
+        return result.iloc[0].to_dict()
+
+
+"""
+Lookup tables have three groups of columns:
+1) Columns containing wildcards that will match any filter value provided during lookup
+2) Columns that don't contain wildcards, but will still be used for filtering during lookup
+3) Columns that are only part of the result of a lookup
+
+We assume the set of values provided during lookup always matches column sets 1+2
+"""
+
+
+class LookupTable:
+    """
+    Construct a lookup table.
+    - df: The original dataframe
+    - defaults: Columns containing wildcards that match any filter value (e.g. '*' or 'DEFAULTS'); keys are column names, values are the wildcard value
+    - filterable: Names of any other columns on which filtering will be applied
+    """
+
+    def __init__(
+        self, df: pd.DataFrame, defaults: dict[str, any], filterable: list[str]
+    ):
+        self._defaults = defaults
+        self._filterable = filterable
+
+        df = df.reset_index()
+
+        counter = pd.Series(0, df.index)
+        for key, default_value in self._defaults.items():
+            counter = counter + (df[key] == default_value)
+        df["_DefaultCount"] = counter
+
+        if len(filterable) > 0:
+            grouped = df.sort_values("_DefaultCount").groupby(filterable, sort=False)
+            self._data = {
+                key: LookupSubtablePandas(grouped.get_group(key), defaults)
+                for key in grouped.groups
+            }
+        else:
+            self._data = LookupSubtablePandas(df.sort_values("_DefaultCount"), defaults)
+
+    """
+    Lookup the best matching row from the original dataframe.
+    - filter: Mapping of columns to values; the columns must match the combination of 'defaults' and 'filterable' provided when the table was initialized
+    """
+
+    def lookup(self, filter: dict[str, any]):
+        # Select the right subtable
+        if len(self._filterable) > 0:
+            group_keys = [filter[x] for x in self._filterable]
+            group_keys = tuple(group_keys) if len(group_keys) > 1 else group_keys[0]
+            subtable = self._data[group_keys]
+        else:
+            subtable = self._data
+
+        # Find the remaining filter items
+        remaining_filter = {
+            key: value for key, value in filter.items() if key in self._defaults
+        }
+        return subtable.lookup(remaining_filter)
diff --git a/optimization202/ESUPS_case_study/src/path.py b/optimization202/ESUPS_case_study/src/path.py
new file mode 100644
index 0000000..ead622d
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/path.py
@@ -0,0 +1,7 @@
+from pathlib import Path
+
+WORKSPACE_DIR = Path(__file__).parent.parent.resolve()
+DATA_DIR = WORKSPACE_DIR / "data"
+TEST_DATA_DIR = WORKSPACE_DIR / "data" / "test_data"
+DASHBOARD_OUTPUT_PATH = WORKSPACE_DIR / "dashboard_output"
+
diff --git a/optimization202/ESUPS_case_study/src/reading.py b/optimization202/ESUPS_case_study/src/reading.py
new file mode 100644
index 0000000..e067225
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/reading.py
@@ -0,0 +1,534 @@
+import math
+import os
+from glob import glob
+from typing import Tuple
+
+import pandas as pd
+
+from src.data import (
+    Country,
+    Dataset,
+    Depot,
+    Disaster,
+    DisasterImpact,
+    DisasterLocation,
+    DisasterType,
+    DistanceInfo,
+    Item,
+    Location,
+    TransportMode,
+)
+from src.lookup import LookupTable
+
+
+class CsvProblemReader:
+    def read(self, folder: str, min_year: int = 0, max_year: int = 9999) -> Dataset:
+        countries = self._read_countries(os.path.join(folder, "countryContinents.csv"))
+
+        # Disasters filtered by type/year
+        disaster_types = self._read_disaster_type_selection(
+            os.path.join(folder, "disasterTypeSelection.csv")
+        )
+        disaster_coordinates = self._read_disaster_coordinates(
+            os.path.join(folder, "disasterCoordinates.csv")
+        )
+        (disasters, disaster_locations, disaster_totals) = self._read_impact_data(
+            os.path.join(folder, "disasters.csv"),
+            disaster_types,
+            countries,
+            disaster_coordinates,
+            min_year,
+            max_year,
+        )
+
+        # Items with attributes
+        items = self._read_items(os.path.join(folder, "items.csv"))
+
+        # Depots and inventory
+        depot_selection = self._read_depot_selection(
+            os.path.join(folder, "depotSelection.csv")
+        )
+        depot_mapping = self._read_depot_mapping(
+            os.path.join(folder, "depotMapping.csv")
+        )
+        depots = self._read_depot_coordinates(
+            os.path.join(folder, "depotCoordinates.csv"), depot_selection, countries
+        )
+
+        inventory_scenarios = {
+            os.path.basename(path): self._read_inventory(
+                path, depots, depot_mapping, items
+            )
+            for path in glob(os.path.join(folder, "inventory", "*.csv"))
+        }
+
+        inventory = inventory_scenarios["actual.csv"]
+
+        ability_to_respond = self._read_ability_to_respond(
+            os.path.join(folder, "abilityToRespond.csv"), countries.values(), items
+        )
+
+        (transport_modes, distances) = self._read_distance_matrix(
+            os.path.join(folder, "distanceMatrix.csv"),
+            os.path.join(folder, "transportModes.csv"),
+            os.path.join(folder, "transportParameters.csv"),
+            depots,
+            disaster_locations,
+        )
+
+        (
+            people_affected,
+            persons_per_item_general,
+            persons_per_item_monthly,
+        ) = self._read_demand(
+            os.path.join(folder, "personsPerItem.csv"),
+            disasters,
+            items,
+            ability_to_respond,
+        )
+
+        probabilities = {disaster: 1 / len(disasters) for disaster in disasters}
+
+        return Dataset(
+            depots,
+            disasters,
+            disaster_locations,
+            probabilities,
+            items,
+            transport_modes,
+            inventory,
+            inventory_scenarios,
+            distances,
+            people_affected,
+            persons_per_item_general,
+            persons_per_item_monthly,
+            disaster_totals,
+        )
+
+    def _read_disaster_coordinates(self, path: str) -> dict[str, str]:
+        return pd.read_csv(path).set_index("gglAddressAscii").to_dict("index")
+
+    def _read_disaster_type_selection(self, path: str) -> list[DisasterType]:
+        data = pd.read_csv(path)
+        filtered = data[data["include"] == 1]
+        return [DisasterType(id) for id in filtered["disasterType"].tolist()]
+
+    def _read_impact_data(
+        self,
+        path: str,
+        disaster_types: list[DisasterType],
+        countries: dict[str, Country],
+        disaster_coordinates: dict[str, any],
+        min_year: int,
+        max_year: int,
+    ) -> tuple[list[Disaster], list[DisasterLocation], dict[str, int]]:
+        disaster_type_lookup = {
+            disaster_type.id: disaster_type for disaster_type in disaster_types
+        }
+        disaster_type_keys = disaster_type_lookup.keys()
+
+        data = pd.read_csv(path)
+
+        # Filter by provided disaster types
+        data = data[data["Type"].isin(disaster_type_keys)]
+
+        # Filtered by year rage
+        data = data[(data["Year"] >= min_year) & (data["Year"] <= max_year)]
+
+        # Filter out rows with NULL for affected people
+        data = data[~data["TotAffected"].isna()]
+
+        disaster_affected_totals = data.groupby("DisasterID")["TotAffected"].sum()
+        disaster_affected_totals = disaster_affected_totals.to_dict()
+
+        disasters: list[Disaster] = []
+        locations: dict[str, DisasterLocation] = {}
+
+        grouped_data = data.groupby("DisasterID")
+
+        for disaster_id, rows in grouped_data:
+            impacted_locations = []
+
+            row = rows.iloc[0]
+            disaster = Disaster(
+                disaster_id,
+                disaster_type_lookup[row["Type"]],
+                row["Day"],
+                row["Month"],
+                row["Year"],
+                impacted_locations,
+            )
+
+            for index, row in enumerate(rows.to_dict("records")):
+                address = row["gglAddress"]
+                if address not in locations:
+                    location = DisasterLocation(
+                        address,
+                        address,
+                        countries[row["gglCountry"]],
+                        disaster_coordinates[address]["gglLat"],
+                        disaster_coordinates[address]["gglLong"],
+                    )
+                    locations[address] = location
+                else:
+                    location = locations[address]
+
+                sub_location_id = "SubLoc_{0:05}".format(index)
+                impacted_locations.append(
+                    DisasterImpact(
+                        f"{disaster_id}:{sub_location_id}",
+                        disaster,
+                        location,
+                        index,
+                        row["TotAffected"],
+                    )
+                )
+
+            disasters.append(disaster)
+
+        return (disasters, list(locations.values()), disaster_affected_totals)
+
+    def _read_countries(self, path: str) -> dict[str, Country]:
+        data = pd.read_csv(path)
+        return {
+            row.gglCountry: Country(row.gglCountry, row.Continent)
+            for row in data.itertuples()
+        }
+
+    def _read_depot_selection(self, path: str) -> list[str]:
+        data = pd.read_csv(path)
+        filtered = data[data["include"] == 1]
+        return filtered["gglAddressAscii"].tolist()
+
+    def _read_depot_mapping(self, path: str) -> dict[str, str]:
+        data = pd.read_csv(path)
+        return data.set_index("gglAddressAsciiMapFrom")[
+            "gglAddressAsciiMapTo"
+        ].to_dict()
+
+    def _read_depot_coordinates(
+        self, path: str, depot_selection: list[str], countries: dict[str, Country]
+    ) -> list[Depot]:
+        data = pd.read_csv(path)
+        data = data[data["gglAddressAscii"].isin(depot_selection)]
+        return [
+            Depot(
+                row.gglAddressAscii,
+                row.gglAddressAscii,
+                countries[row.gglCountryAscii],
+                row.gglLat,
+                row.gglLong,
+            )
+            for row in data.itertuples()
+        ]
+
+    def _read_inventory(
+        self,
+        path: str,
+        depots: list[Depot],
+        depot_mapping: dict[str, str],
+        items: list[Item],
+    ) -> dict[Tuple[Depot, Item], int]:
+        data = pd.read_csv(path)
+
+        # Apply address mapping
+        data["gglAddress"] = data["gglAddress"].replace(depot_mapping)
+
+        # Group by address and item name
+        data = data.groupby(["ItemName", "gglAddress"])[["Total"]].sum().reset_index()
+
+        item_lookup = {item.id: item for item in items}
+        depot_lookup = {depot.address: depot for depot in depots}
+
+        return {
+            (depot_lookup[row.gglAddress], item_lookup[row.ItemName]): row.Total
+            for row in data.itertuples()
+        }
+
+    def _read_items(self, path: str) -> list[Item]:
+        data = pd.read_csv(path)
+        # TODO Ensure all inventory items have attributes
+
+        result = [
+            Item(row.ItemName, row.WeightMetricTon, row.CubicMeters)
+            for row in data.itertuples()
+        ]
+        return result
+
+    def _read_distance_matrix(
+        self,
+        path_distances: str,
+        path_modes: str,
+        path_params: str,
+        depots: list[Depot],
+        disaster_locations: list[DisasterLocation],
+    ) -> Tuple[
+        list[TransportMode],
+        dict[Tuple[Location, Location, TransportMode], DistanceInfo],
+    ]:
+        distances = pd.read_csv(path_distances)
+        modes = pd.read_csv(path_modes)
+        params = pd.read_csv(path_params)
+
+        transport_modes = [
+            TransportMode(
+                row.Mode,
+                row.DistanceMethod,
+                row.BigMCostElim,
+                row.MaxDrivingTimeCutAboveHrs,
+            )
+            for row in modes.itertuples()
+        ]
+
+        params = params.pivot(
+            index=["Mode", "gglAddress"], columns="Attribute", values="Number"
+        )
+
+        depot_lookup = {depot.address: depot for depot in depots}
+        location_lookup = {
+            location.address: location for location in disaster_locations
+        }
+
+        pairs = {
+            (
+                depot_lookup[row.depotGglAddressAscii],
+                location_lookup[row.disasterGglAddressAscii],
+            ): (
+                self._calc_distance_latlong(
+                    row.depotGglLat,
+                    row.depotGglLong,
+                    row.disasterGglLat,
+                    row.disasterGglLong,
+                ),
+                float(row.distance_km),
+                float(row.drivingTime_hrs),
+            )
+            for row in distances.itertuples()
+            if row.depotGglAddressAscii in depot_lookup
+            and row.disasterGglAddressAscii in location_lookup
+        }
+
+        if len(pairs) == 0:
+            raise RuntimeError(f"Empty distance matrix encountered")
+
+        # TODO Check that inventory depot names are in 'depots'
+        # TODO Check that tot.affected disaster names are in 'disasters'
+        # TODO Discuss: Distances CSV contains lat/lng too; why not use those!?
+        params_lookup = LookupTable(params, {"gglAddress": "DEFAULT"}, ["Mode"])
+        matrix = {
+            (depot, disaster, transport_mode): DistanceInfo(
+                *self._calc_distance_travel_time_cost(
+                    spherical_distance,
+                    google_distance,
+                    driving_time_hrs,
+                    depot,
+                    params_lookup,
+                    transport_mode,
+                ),
+            )
+            for (depot, disaster), (
+                spherical_distance,
+                google_distance,
+                driving_time_hrs,
+            ) in pairs.items()
+            for transport_mode in transport_modes
+        }
+
+        return (transport_modes, matrix)
+
+    def _read_ability_to_respond(
+        self, path: str, countries: list[Country], items: list[Item]
+    ) -> dict[Tuple[Country, Item], float]:
+        data = pd.read_csv(path).set_index(["gglCountry", "item"])["capacityToRespond"]
+
+        result = {
+            (country, item): data.loc[country.id, "DEFAULT"]
+            for country in countries
+            for item in items
+        }
+
+        return result
+
+    def _add_counter(self, df: pd.DataFrame) -> pd.DataFrame:
+        result = []
+        counter = 0
+        previous = None
+        for i in range(len(df)):
+            current = df[i]
+            counter = counter + 1 if previous == current else 0
+            result.append(counter)
+            previous = current
+        return pd.DataFrame(result)
+
+    def _calc_distance_latlong(self, lat1, long1, lat2, long2):
+        # Convert latitude and longitude to
+        # spherical coordinates in radians.
+
+        # Add noise to lat and long so that no div 0 errror if same spot
+        lat1 = lat1 + 1e-7
+        long1 = long1 + 1e-7
+
+        degrees_to_radians = math.pi / 180.0
+
+        # phi = 90 - latitude
+        phi1 = (90.0 - lat1) * degrees_to_radians
+        phi2 = (90.0 - lat2) * degrees_to_radians
+
+        # theta = longitude
+        theta1 = long1 * degrees_to_radians
+        theta2 = long2 * degrees_to_radians
+
+        # Compute spherical distance from spherical coordinates.
+
+        cos = math.sin(phi1) * math.sin(phi2) * math.cos(theta1 - theta2) + math.cos(
+            phi1
+        ) * math.cos(phi2)
+
+        arc = math.acos(cos)
+
+        # Remember to multiply arc by the radius of the earth
+        # in your favorite set of units to get length.
+        return arc * 6378.1
+
+    def _calc_distance_travel_time_cost(
+        self,
+        spherical_distance: float,
+        google_distance: float,
+        google_driving_time: float,
+        depot: Depot,
+        params_lookup: LookupTable,
+        transport_mode: TransportMode,
+    ) -> Tuple[float, float, float]:
+        default_params = {
+            "fixedTime": 0,
+            "StretchTimeFactor": 1,
+            "RealKm_per_CrowKm": 1,
+        }
+        # TODO Try retrieving by depot name first (proper 'wildcard lookup')
+        params = default_params | params_lookup.lookup(
+            {"Mode": transport_mode.id, "gglAddress": depot.address}
+        )
+        result_distance = spherical_distance
+
+        # TODO Document the expected columns for 'params'
+
+        if transport_mode.distance_method == "google":
+            result_distance = google_distance
+            base_time = google_driving_time
+        elif transport_mode.distance_method == "crowScale":
+            result_distance = spherical_distance * params["RealKm_per_CrowKm"]
+            base_time = result_distance / params["SpeedKmPerHr"]
+        else:
+            raise NameError("You don't have a proper distance method.")
+
+        if base_time >= transport_mode.max_driving_time_cut_above_hrs:
+            (time, cost_per_ton) = (
+                transport_mode.big_m_cost_elim,
+                transport_mode.big_m_cost_elim,
+            )
+        else:
+            time = params["FixedAddlTime_Hrs"] + base_time * params["StretchTimeFactor"]
+            cost_per_ton = (
+                params["FixedAddlCost_USD"]
+                + result_distance
+                * params["StretchDistanceFactor"]
+                * params["VarCost_USD_ton_km"]
+            )
+
+        return (result_distance, time, cost_per_ton)
+
+    def _read_demand(
+        self,
+        path_demand: str,
+        disasters: list[Disaster],
+        items: list[Item],
+        ability_to_respond: dict[Tuple[Country, Item], float],
+        zero_demand_threshold: float = 1000000,
+    ) -> Tuple[
+        dict[Tuple[DisasterImpact, Item], float],
+        dict[Tuple[DisasterImpact, Item], float],
+        dict[Tuple[DisasterImpact, Item], float],
+    ]:
+        df = pd.read_csv(path_demand)
+
+        people_affected = {
+            (impact, item): max(
+                0,
+                impact.total_affected
+                - ability_to_respond[impact.location.country, item],
+            )
+            for disaster in disasters
+            for impact in disaster.impacted_locations
+            for item in items
+        }
+
+        lookup = LookupTable(
+            df,
+            {"Disaster Type": "DEFAULT", "gglCountry": "DEFAULT", "Month": "DEFAULT"},
+            ["Item"],
+        )
+
+        persons_per_item_general = {
+            (impact, item): lookup.lookup(
+                {
+                    "Item": item.id,
+                    "Disaster Type": disaster.type.id,
+                    "Month": -1,
+                    "gglCountry": impact.location.country.id,
+                }
+            )["PersonsPerItem"]
+            for disaster in disasters
+            for impact in disaster.impacted_locations
+            for item in items
+        }
+
+        persons_per_item_monthly = {
+            (impact, item): lookup.lookup(
+                {
+                    "Item": item.id,
+                    "Disaster Type": disaster.type.id,
+                    "Month": disaster.month if disaster.month in range(1, 13) else -1,
+                    "gglCountry": impact.location.country.id,
+                }
+            )["PersonsPerItem"]
+            for disaster in disasters
+            for impact in disaster.impacted_locations
+            for item in items
+        }
+
+        return (
+            people_affected,
+            persons_per_item_general,
+            persons_per_item_monthly,
+        )
+
+
+class DatasetManager:
+    _datasets: list[str]
+    _cache: dict[str, Dataset] = {}
+
+    def __init__(self, path="data"):
+        self._root = path
+        self._datasets = self.get_immediate_subdirectories(path)
+        self._reader = CsvProblemReader()
+
+    def get_immediate_subdirectories(self, parent_dir: str):
+        return [
+            name
+            for name in os.listdir(parent_dir)
+            if os.path.isdir(os.path.join(parent_dir, name))
+        ]
+
+    def list_dataset_keys(self):
+        return list(self._datasets)
+
+    def get_dataset(self, key: str) -> Dataset:
+        if key in self._cache:
+            return self._cache[key]
+
+        if key not in self._datasets:
+            raise Exception(f'Unknown dataset "{key}"')
+
+        dataset = self._reader.read(os.path.join(self._root, key))
+        self._cache[key] = dataset
+        return dataset
diff --git a/optimization202/ESUPS_case_study/src/solving.py b/optimization202/ESUPS_case_study/src/solving.py
new file mode 100644
index 0000000..9c25883
--- /dev/null
+++ b/optimization202/ESUPS_case_study/src/solving.py
@@ -0,0 +1,310 @@
+from dataclasses import dataclass
+from enum import IntEnum
+from typing import Tuple, Union
+
+import gurobipy as gp
+from gurobipy import GRB, tupledict
+
+from src.data import Depot, Disaster, DisasterImpact, Location, TransportMode
+
+
+class AllocationStrategy(IntEnum):
+    MinimizeTwoStage = 0
+    MinimizeFixedInventory = 1
+    WorstDepot = 2
+
+
+@dataclass(frozen=True)
+class SolverParameters:
+    """
+    Parameters that influence how the solver transforms the Problem into a mathematical model.
+
+    Attributes
+    ----------
+    allocation_strategy
+        If and how inventory can be reallocated
+    scale_demand
+        Whether demand should be scaled (down) to not exceed supply
+    """
+
+    allocation_strategy: AllocationStrategy = (
+        AllocationStrategy.MinimizeFixedInventory,
+    )
+    scale_demand: bool = False
+
+
+CostMatrix = dict[Tuple[Location, Location, TransportMode], float]
+
+
+@dataclass(frozen=True)
+class Problem:
+    depots: list[Depot]
+    inventory: dict[Depot, int]
+    demand: dict[DisasterImpact, float]
+    disasters: list[Disaster]
+    probabilities: dict[Disaster, float]
+    transport_modes: list[TransportMode]
+    cost: CostMatrix
+
+
+@dataclass(frozen=True)
+class Solution:
+    """
+    Result of solving a single Problem with specific SolverParameters
+
+    Attributes
+    ----------
+    total_cost_inc_dummy
+        Total transportation cost including artificial cost for using the dummy node (myObj)
+    total_cost_exc_dummy
+        Total transportation cost from the real depots (myObjNoDum)
+    total_demand
+        Total demand in the input data (myWeightedDemand)
+    covered_demand_exc_dummy
+        Demand served from real depots, averaged over all scenarios (myWeightedDemandMetNoDum)
+    fraction_of_disasters_using_dummy
+        Fraction of disaster scenarios for which not enough real inventory is available (myFractionOfDisastersUsingDummy)
+    duals_inventory_exc_dummy_plus_dummy_cost
+        Adjusted dual variables for the inventory constraints (values are independent of dummy costs) (dualsInvNoDum_PlusDummyCost)
+    duals_inventory_exc_dummy_unadjusted
+        Original dual variables for the inventory constraints, aggregated over disaster scenarios (dualsInvNoDum_UnAdj)
+    duals_inventory_exc_dummy_all
+        All original dual variables for the inventory constraints (dualsInvNoDum_All)
+    flow_exc_dummy
+        Allocation of depot inventory to disaster locations in each scenario, excluding the dummy depot (myFlowNoDum)
+    flow
+        Allocation of depot inventory to disaster locations in each scenario (myFlow)
+    optimal_inventory
+        Optimal or fixed allocation of inventory to depots (myOptInvNoDum)
+    dual_total_inventory
+        Dual variable for the total inventory constraint (dualTotInv)
+    """
+
+    total_cost_inc_dummy: float
+    total_cost_exc_dummy: float
+    total_demand: float
+    covered_demand_exc_dummy: float
+    fraction_of_disasters_using_dummy: float
+    duals_inventory_exc_dummy_plus_dummy_cost: dict[Depot, float]
+    duals_inventory_exc_dummy_unadjusted: dict[Depot, float]
+    duals_inventory_exc_dummy_all: dict[Tuple[Disaster, Depot], float]
+    flow_exc_dummy: dict[Tuple[Disaster, Depot, DisasterImpact, TransportMode], float]
+    flow: dict[Tuple[Disaster, Depot, DisasterImpact, TransportMode], float]
+    optimal_inventory: dict[Depot, float]
+    dual_total_inventory: float
+
+    _dummy_depot: Depot
+
+
+class StochasticSolver:
+    _threshold_cost_elim: float = 1e9
+    _threshold_cost_dummy: float = 1e9
+
+    def __init__(self):
+        self._dummy = Location("DUMMY", "", "", 0, 0)
+        self._env = gp.Env(params={"OutputFlag": 0, "Threads": 1})
+
+    def dispose(self):
+        self._env.dispose()
+        self._env = None
+
+    def solve(self, problem: Problem, parameters: SolverParameters) -> Solution:
+        sources = problem.depots + [self._dummy]
+
+        demand = (
+            self._scale_demand(problem) if parameters.scale_demand else problem.demand
+        )
+
+        arcs = gp.tuplelist(
+            [
+                (k, i, j, v)
+                for i in sources
+                for k in problem.disasters
+                for j in k.impacted_locations
+                for v in problem.transport_modes
+                if (
+                    self._get_arc_cost(problem.cost, i, j, v)
+                    < self._threshold_cost_elim
+                )
+                or (i == self._dummy)
+            ]
+        )
+
+        arc_cost = {
+            (k, i, j, v): self._get_arc_cost(problem.cost, i, j, v)
+            * problem.probabilities[k]
+            for (k, i, j, v) in arcs
+        }
+
+        model = gp.Model("StochLP", env=self._env)
+
+        # First stage variable: Quantity to be allocated to each depot
+        x: tupledict[Depot, gp.Var] = model.addVars(problem.depots, lb=0, name="x")
+
+        # Second stage variable: Quantity transported from (real or dummy) depot to disaster locations using each mode of transport
+        y: tupledict[
+            Tuple[Disaster, Union[Depot, Location], DisasterImpact, TransportMode],
+            gp.Var,
+        ] = model.addVars(arcs, lb=0, obj=arc_cost, name="y")
+
+        # Constraint: Total incoming arc flow must cover demand for each disaster location
+        model.addConstrs(
+            (
+                y.sum(k, "*", j, "*") == demand[j]
+                for k in problem.disasters
+                for j in k.impacted_locations
+            ),
+            name="satisfyDemand",
+        )
+
+        # Constraint: Total outgoing arc flow must match initial or reallocated inventory
+        inventory_balance: tupledict[
+            Tuple[Disaster, Depot], gp.Constr
+        ] = model.addConstrs(
+            (
+                y.sum(k, i, "*", "*") <= x[i]
+                for k in problem.disasters
+                for i in problem.depots
+            ),
+            name="satisfySupply",
+        )
+
+        # Constraint: Ensure inventory reallocation matches total existing inventory
+        total_initial_inventory = sum(problem.inventory.values())
+        match_total_inventory = model.addConstr(x.sum() == total_initial_inventory)
+
+        def fix_inventory_balance(values: dict[Disaster, float]):
+            for key, value in values.items():
+                x[key].LB = x[key].UB = value
+
+        if parameters.allocation_strategy == AllocationStrategy.MinimizeFixedInventory:
+            fix_inventory_balance(problem.inventory)
+        elif parameters.allocation_strategy == AllocationStrategy.WorstDepot:
+            worst_depot = None
+            worst_objective = -1e100
+            for depot in problem.depots:
+                centralized_inventory = {
+                    other: total_initial_inventory if other == depot else 0
+                    for other in problem.depots
+                }
+                fix_inventory_balance(centralized_inventory)
+                model.optimize()
+                if model.Status != GRB.Status.OPTIMAL:
+                    raise RuntimeError("Could not solve model to optimality")
+                if model.ObjVal > worst_objective:
+                    worst_depot = depot
+                    worst_objective = model.ObjVal
+            centralized_inventory = {
+                other: total_initial_inventory if other == worst_depot else 0
+                for other in problem.depots
+            }
+            fix_inventory_balance(centralized_inventory)
+
+        model.optimize()
+        if model.Status != GRB.Status.OPTIMAL:
+            raise RuntimeError("Could not solve model to optimality")
+
+        # Total transport cost
+        total_cost_inc_dummy = model.ObjVal
+        dummy_cost = sum(
+            var.X * var.Obj for var in y.select("*", self._dummy, "*", "*")
+        )
+        total_cost_exc_dummy = total_cost_inc_dummy - dummy_cost
+
+        # Demand met without using the dummy node
+        covered_demand_by_dummy = sum(
+            y[k, i, j, v].X * problem.probabilities[k]
+            for (k, i, j, v) in arcs.select("*", self._dummy, "*", "*")
+        )
+        total_demand = sum(
+            local_demand * problem.probabilities[j.disaster]
+            for (j, local_demand) in demand.items()
+        )
+        covered_demand_exc_dummy = total_demand - covered_demand_by_dummy
+
+        # Flow in solution
+        solution_y = {key: y[key].X for key in arcs}
+
+        # Fraction of disaster scenarios in which the dummy supply is used
+        fraction_of_disasters_using_dummy = len(
+            [
+                disaster
+                for disaster in problem.disasters
+                if sum(
+                    solution_y[key]
+                    for key in arcs.select(disaster, self._dummy, "*", "*")
+                )
+                > 0
+            ]
+        ) / len(problem.disasters)
+
+        # Dual variables for the inventory balance constraints
+        dual_correction = fraction_of_disasters_using_dummy * self._threshold_cost_dummy
+        duals_inventory_exc_dummy_unadjusted = {
+            i: sum(inventory_balance[k, i].Pi for k in problem.disasters)
+            for i in problem.depots
+        }
+        duals_inventory_exc_dummy_plus_dummy_cost = {
+            i: dual_correction + pi
+            for (i, pi) in duals_inventory_exc_dummy_unadjusted.items()
+        }
+        duals_inventory_exc_dummy_all = {
+            (k, i): constr.Pi for (k, i), constr in inventory_balance.items()
+        }
+
+        flow = {(k, i, j, v): var.X for (k, i, j, v), var in y.items() if var.X > 0}
+
+        flow_exc_dummy = {
+            (k, i, j, v): value
+            for (k, i, j, v), value in flow.items()
+            if i != self._dummy
+        }
+
+        optimal_inventory = {depot: var.X for depot, var in x.items()}
+
+        dual_total_inventory = (
+            match_total_inventory.Pi
+            if parameters.allocation_strategy
+            == AllocationStrategy.MinimizeFixedInventory
+            else None
+        )
+
+        return Solution(
+            total_cost_inc_dummy,
+            total_cost_exc_dummy,
+            total_demand,
+            covered_demand_exc_dummy,
+            fraction_of_disasters_using_dummy,
+            duals_inventory_exc_dummy_plus_dummy_cost,
+            duals_inventory_exc_dummy_unadjusted,
+            duals_inventory_exc_dummy_all,
+            flow_exc_dummy,
+            flow,
+            optimal_inventory,
+            dual_total_inventory,
+            self._dummy,
+        )
+
+    def _get_arc_cost(
+        self,
+        cost: CostMatrix,
+        source: Location,
+        target: DisasterImpact,
+        mode: TransportMode,
+    ):
+        if source == self._dummy:
+            return self._threshold_cost_dummy
+        cell = cost.get((source, target.location, mode))
+        return self._threshold_cost_elim if cell == None else cell
+
+    def _scale_demand(self, problem: Problem) -> dict[DisasterImpact, float]:
+        supply = sum(problem.inventory.values())
+        result: dict[DisasterImpact, float] = {}
+        for disaster in problem.disasters:
+            total_demand = sum(
+                problem.demand[location] for location in disaster.impacted_locations
+            )
+            factor = min(1, supply / total_demand) if total_demand > 1e-6 else 1
+            for location in disaster.impacted_locations:
+                result[location] = factor * problem.demand[location]
+        return result