Transport Optimization - Gurobi + PyQt6

Desktop application to solve the classic transportation problem in operations research, using the Gurobi optimizer and a PyQt6 graphical interface.

Description

The transportation problem is a linear optimization problem that consists of minimizing the total cost of transporting products from multiple factories to multiple warehouses, while respecting capacity and demand constraints.

This application allows you to:

• Define the number of factories and warehouses
• Enter the production capacity of each factory
• Enter the demand for each warehouse
• Define the unit transportation cost matrix
• Solve the optimization problem
• Visualize the optimal solution
• Export results to CSV

Features

• Intuitive and professional graphical interface
• Flexible configuration (1-10 factories and warehouses)
• Optimal resolution with Gurobi
• Supply/demand balance verification
• Detailed display of optimal flows
• Minimum total cost calculation
• Export results to CSV format
• Tables with scroll areas for large instances

Prerequisites

Required Software

• Python 3.8+
• Gurobi Optimizer (academic or commercial license)
• PyQt6

Installation

1. Clone the project

git clone https://github.com/your-username/transport-optimizer.git
cd transport-optimizer

2. Install Python dependencies

pip install PyQt6 gurobipy

3. Gurobi Configuration

You must have a valid Gurobi license. To obtain a free academic license:

• Visit Gurobi Academic License
• Download and install Gurobi
• Activate your license with grbgetkey

Usage

Launch the application

python transport_app.py

User Guide

1. Problem Configuration

   • Define the number of factories (sources)
   • Define the number of warehouses (destinations)
   • Click on "Create Tables"

2. Data Entry

   • Enter the production capacity of each factory (supply)
   • Enter the demand for each warehouse
   • Fill in the unit transportation cost matrix

3. Resolution

   • Click on "Solve Problem"
   • The application verifies that the problem is balanced (total supply = total demand)
   • The optimal solution is displayed automatically

4. Results Review

   • View the optimal transportation flows
   • Check the minimum total cost
   • Export results to CSV if needed

Technologies Used

• Python: Programming language
• PyQt6: Framework for graphical interface
• Gurobi: Linear optimization solver
• CSV: Results export format