semihalperdundar/Optimization-for-Food-Program-in-Ethiopia

# World Food Programme Supply Chain Optimization This repository contains optimization models developed for the United Nations **World Food Programme (WFP)** to design an optimal food supply chain for Ethiopia over a **30-day period**. The project applies **Linear Programming (LP)** and **Mixed-Integer Linear Programming (MILP)** techniques using **CVXPY** to determine the best procurement, transportation, and distribution strategies. ## 📂 Project Structure ``` ├── Basic_Linear_Programming_Model_Scenario_1.py ├── Basic_Linear_Programming_Model_Scenario_2.py ├── Mixed_Integer_Lineer_Programming_Model.py ├── README.md ``` ### 📌 **Files Overview** - **Basic_Linear_Programming_Model_Scenario_1.py** → LP model for Scenario 1, solving the base case with defined constraints. - **Basic_Linear_Programming_Model_Scenario_2.py** → LP model for Scenario 2, including variations such as demand increase and port capacity reductions. - **Mixed_Integer_Lineer_Programming_Model.py** → MILP model, integrating binary decision variables for realistic supplier activations. - **SupplyChain_LP_Report_Edited.docx** → Project report detailing model formulation, results, and sensitivity analysis. - **Assignment OPT for DS.pptx** → Presentation outlining the problem, approach, and findings. ## 🎯 Objective The goal of this project is to **minimize total supply chain costs** while ensuring that all food demand and nutritional requirements are met for Ethiopian beneficiaries. The model incorporates: - **Procurement planning**: Optimal allocation from multiple suppliers - **Transportation logistics**: Cost-effective sea and land routing - **Warehouse and port capacities**: Operational constraints - **Nutritional targets**: Satisfying dietary requirements for all camps - **Scenario analysis**: Testing model robustness under real-world uncertainties ## 🔬 Methodology - **Linear Programming (LP)**: Used to model base scenarios with continuous decision variables. - **Mixed Integer Linear Prog …