esups and readme

optimization202/ESUPS_case_study/README.md

@@ -1,46 +1,20 @@
-# Technician Assignment With Mathematical Optimization
+# Emergency Supply Prepositioning Strategy (ESUPS) Case Study
+
+To reduce gaps and overlaps in emergency preparedness, [ESUPS](https://esups.org/) promotes coordinated prepositioning of emergency supplies and collective logistics strategies.
 
 ## Problem
-You are consulting for a telecom company that dispatches technicians to perform various tasks for customers, 
-ranging from equipment installations and setups to repairs. Considering various constraints such as:
-- Technician availability
-- Skillset matching
-- Customer time window
-- Service priority
-- Start and return of technicians to the central depot
-- Limited resources/vans
+By using scenario-based transportation linear programming model, the model resource allocation and logistics decisions, providing quantitative metrics to evaluate and improve humanitarian operations.
 
 The goal is to decide:
-- Which technicians are assigned to which customers and in what sequence?
-- How to allocate the limited resources, ensuring each technician has access to a van?
-
-We use a two-step solution approach, using Mathematical Optimization (MO) in both, to solve this problem.
-
-## Repo Guide
-- The first model is called _Technician Routing_ where we decide which technicians are assigned to which customers 
-and in what sequence. This model is described and solved in the [technician_routing](technician_routing.ipynb) notebook.
-- The second model is called _Resource Assignment_ where we decide which technicians should be assigned to which vans.
-  This model is described and solved in the `resource_assignment` notebook. 
-  - There are two versions of this notebook: a partial version ([resource_assignment](resource_assignment.ipynb)) 
-    that can be used for teaching, and one with the complete description, formulation, and the code ([resource_assignment_complete](resource_assignment_complete.ipynb)). 
-- The [data-Sce0.xlsx](data-Sce0.xlsx) is the input used for the _Technician Routing_ model.
-- The [routes.csv](routes.csv) and [orders.csv](orders.csv) are the outputs of _Technician Routing_ model 
-  and are used as inputs for the _Resource Assignment_ model. Of course, you can run the `technician_routing` notebook and 
-  or even modify it as you please to get new results. Ensure the format of the output is similar
-  in case you like to use the `resource_assignment` notebook afterward.
-
-The model size is intentionally small to ensure even those without access to a Gurobi license can run the model (check out [License Requirement](#license-requirement) section for more info about the license).
+- How much of each supply to store in certain locations
+- How to respond to disaster events at minimal cost
 
 ## Google Colab
-If you like to access the notebooks in Google Colab, click: 
-- [technician_routing](https://colab.research.google.com/github/decision-spot/technician_assignment/blob/main/technician_routing.ipynb)
-- [resource_assignment](https://colab.research.google.com/github/decision-spot/technician_assignment/blob/main/resource_assignment_complete.ipynb)
+[Google Colab Link - ESUPS Case Study](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/ESUPS_case_study/disaster_prepositioning.ipynb)
 
 Note that you must sign in with a Google account to be able to run the code in Colab.
 
-## License Requirement
-The problem is modeled using Gurobi Python API. So, a Gurobi license is required to run this model.
-If you don't have a license, you can request a free commercial evaluation license 
-or a free academic license [here](https://www.gurobi.com/downloads/).
-The data used in the models is small enough to run the model with a Gurobi restricted license
-(available via `pip install gurobi` as shown in the notebook).
+----
+For details on licensing or on running the notebooks, see the overview on [Modeling Examples](../../)
+
+© Gurobi Optimization, LLC

optimization202/Modeling_Session_1/README.md

@@ -11,6 +11,7 @@ We also put **completed** versions of the modeling notebook and the exercise in
 
 
 ## Exercise Set 1 Notebook Links
+Note that you must sign in with a Google account to be able to run the code in Colab.
 
 [Google Colab Link - Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/exercise_set1.ipynb)
 

optimization202/Modeling_Session_2/README.md

@@ -11,6 +11,7 @@ We also put **completed** versions of the modeling notebook and the exercise in
 
 
 ## Exercise Set 2 Notebook Links
+Note that you must sign in with a Google account to be able to run the code in Colab.
 
 [Google Colab Link - Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_2/exercise_set2.ipynb)
 

optimization202/README.md

@@ -6,7 +6,7 @@
 The material is from the [Optimization 202 for Data Scientists](https://www.gurobi.com/events/optimization-203-for-data-scientists/) training, which consists of two modeling examples and a culminating use case. 
 You can review the Jupyter Notebooks here. For all examples, a completed version is available. If you want to test yourself and try out some of the exercises, you should start with the not-completed-versions of the notebooks and then compare with the completed versions.
 
-- Resources for the first [hands-on exercises](Modeling_Session_1)
+- Resources for the **first** [hands-on exercises](Modeling_Session_1)
 
 - Resources for the **second** [hands-on exercises](Modeling_Session_2)