Gurobipy Transportation LP Solver
Project Overview
This project demonstrates how to formulate and solve a transportation Linear Programming (LP) problem using the Gurobi optimization solver with its Python API (gurobipy). It was created as a learning exercise in optimization modeling, exploring LP formulation, constraint setup, and solution retrieval for classic network flow problems such as minimum-cost transportation.
The repository includes: - Python code implementing the transportation LP model. - Utility and helper modules for data handling. - A written report discussing modeling decisions and results.
The transportation problem classically seeks to minimize total shipping cost while satisfying supply and demand constraints across origins and destinations — a staple use case for LP solvers in operations research.
What You’ll Find Inside
- Problem formulation: Setting up decision variables, objective functions, and linear constraints representing supply, demand, and flow balance.
- Gurobi integration: Leveraging the
gurobipyAPI to build and solve the optimization model. - Reports & practice code: Supporting analysis of solver outputs and insights gained while learning LP concepts.