From aa6a09ea4e9616d11d3cb3f7c47b14033160c788 Mon Sep 17 00:00:00 2001
From: Jerry Yurchisin <yurchisin@gurobi.com>
Date: Wed, 13 Nov 2024 18:54:44 -0500
Subject: [PATCH] streamlining

---
 .../opti202_disaster_prepositioning.ipynb     |  93 ++------
 .../workspaces/helper_scripts.py              | 217 ------------------
 .../workspaces/temp_Analyer_workspace.py      | 115 ----------
 .../workspaces/temp_solver_workspace.py       | 200 ----------------
 .../workspaces/temp_worker_workspace.py       | 107 ---------
 5 files changed, 14 insertions(+), 718 deletions(-)
 delete mode 100644 optimization202/ESUPS_case_study/workspaces/helper_scripts.py
 delete mode 100644 optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py
 delete mode 100644 optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py
 delete mode 100644 optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py

diff --git a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb
index 68a3bed..71f136d 100644
--- a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb
+++ b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb
@@ -11,25 +11,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from IPython.display import Image\n",
     "Image(filename='images/ESUPS-Logo.png', width=600)"
@@ -220,16 +204,14 @@
    "outputs": [],
    "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",
+    "                   depot.latitude,\n",
+    "                   depot.longitude] \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",
+    "                  disaster_locations.latitude,\n",
+    "                  disaster_locations.longitude] \n",
     "                  for disaster_locations in dataset.disaster_locations]\n",
     "\n",
     "    dfw = pd.DataFrame(warehouses)\n",
@@ -621,11 +603,9 @@
    "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",
+    "Here is one of the earlier versions of the ESUPS dashboard; even in v1, it 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' mission."
+    "The lesson here: Start with the tools and skills available and always look for ways to improve. "
    ]
   },
   {
@@ -778,7 +758,7 @@
     "        #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.columns=['Var','amount','possible','distance']\n",
     "b.sort_values('distance')"
    ]
   },
@@ -1054,34 +1034,6 @@
     "##### 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": {},
@@ -1122,23 +1074,6 @@
     "**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": {},
@@ -1475,11 +1410,11 @@
     "\\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",
+    "\\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",
+    "& y^k_{ir} &\\geq 0, \\space &\\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",
+    "& x_{i} &\\geq 0 , \\space &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n",
     "\n",
     "\n",
     "\\end{aligned}\n",
diff --git a/optimization202/ESUPS_case_study/workspaces/helper_scripts.py b/optimization202/ESUPS_case_study/workspaces/helper_scripts.py
deleted file mode 100644
index 236b15d..0000000
--- a/optimization202/ESUPS_case_study/workspaces/helper_scripts.py
+++ /dev/null
@@ -1,217 +0,0 @@
-
-#Called in analysis/analysis worker to filter the disasters by time
-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 _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,
-        )
-
-
-
-
-
-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
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py
deleted file mode 100644
index 01299d1..0000000
--- a/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py
+++ /dev/null
@@ -1,115 +0,0 @@
-
-
-#Code for the class
-
-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 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
-
-
-
-
-
-#instanctiate object, might be better to just make the object
-
-parameters = AnalysisParameters()
-
-
-
-analyzer = Analyzer(parameters)
-
-
-#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
-]
-
-
-
-
-#def _run_tasks(self, tasks: list[tuple[str, Dataset, Item]]):
-
-
-
-_solver=StochasticSolver()
-#worker = AnalyzerWorker(self.parameters)
-result = [
-    (filename, format_data(dataset, item,parameters))
-    for (filename, dataset, item) in tasks
-]
-
-
-
-
-
-PROCESSED_INFO= {
-    (filename, analysis.item): analysis
-    for (filename, analysis) in result
-    if analysis is not None
-}
-
-
-
diff --git a/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py
deleted file mode 100644
index 69b77ee..0000000
--- a/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py
+++ /dev/null
@@ -1,200 +0,0 @@
-
-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 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,
-        )
diff --git a/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py
deleted file mode 100644
index a1b5196..0000000
--- a/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#This script will roughly fill in the job that a worker does,
-#it will become a jupyter notebook
-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,
-)
-
-
-def format_data(dataset,item,parameters):
-#Calculate disaster prob
-
-    #dataset = self._filter_dataset(dataset)
-    probabilities = {
-                disaster: 1 / len(dataset.disasters) for disaster in dataset.disasters
-            }
-
-
-
-    #Let's derfine our dictionary to store all the dictionaries
-
-    solutions: dict[SolutionTags, Solution] = {}
-
-    #First we construct the chance each distaster will occur
-    probabilities = {
-                disaster: 1 / len(dataset.disasters) for disaster in dataset.disasters
-            }
-
-    #Now we need to create our cost matrix to tell the solver how much each thing will 
-    #reguire from out budget
-
-    #To do this we will iterate over every item in our dataset and return the designated item
-    cost_matrices={}
-    for key, value in dataset.distance.items():
-        match objective:
-            case SolverObjective.Cost:
-                aa = item.weight * value.cost_per_ton
-
-            case SolverObjective.Time:
-                aa = value.time
-
-            case SolverObjective.Distance:
-                aa = value.distance
-        cost_matrices[key]=aa
-
-
-    #Now we find the inventory in a nice formate
-    inventory = {
-                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
-            }
-
-
-    #Check that there is inventory
-    if sum(inventory.values()) == 0:
-        return None
-
-
-    #Now we get the demand for each item
-
-    #One of the settings we can tweak, need to look more at this
-    source = (
-            dataset.monthly_demand
-            if self.parameters.care_about_month_demand
-            else dataset.general_demand
-        )
-
-    #After the setting is tweeked, we now make the dictionary
-    demand={
-            location: source.get((location, item), 0)
-            for disaster in dataset.disasters
-            for location in disaster.impacted_locations
-        }
-
-    # Solve models for all objectives 
-    for objective in self.parameters.optimization_objectives:
-        #Check all stratigies for each objective
-        for strategy in self.parameters.allocation_strategies:
-            problem = Problem( #from another file
-                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)
-
-    #self._solver = StochasticSolver()