README.md

metadata

title: OperationsResearch
emoji: 🔥
colorFrom: gray
colorTo: yellow
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: false
license: mit
short_description: This project demonstrates the use of Linear Programming (PL)

Operations Research Web Application

This project demonstrates the use of Linear Programming (PL) and Mixed-Integer Linear Programming (PLNE) for solving real-world optimisation problems using Gurobi. It uses Gradio to provide an interactive web interface.

Features

• Production Planning (PL): Optimises the number of products to manufacture for maximum profit under resource constraints.
• Staff Scheduling (PLNE): Mock assignment of employees to shifts based on availability.

Project Structure

.
├── app.py                          # Main entry point of the Gradio application
├── assets/
│   └── compte_rendu.pdf           # Project report
├── models/
│   └── gurobi_models.py           # Gurobi-based solvers for PL and PLNE
├── ui/
│   └── gradio_sections.py         # UI layout and Gradio component logic
├── requirements.txt               # Python dependencies
└── README.md                      # Project documentation

Prerequisites

• Python 3.9 or higher

Environment Setup

1. Clone the repository

git clone https://github.com/KacemMathlouthi/OperationsResearch.git
cd OperationsResearch

2. Create and activate a virtual environment

Linux/macOS

python3 -m venv venv
source venv/bin/activate

Windows

python -m venv venv
venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

Running the Application

Ensure you are in the project root directory and your virtual environment is activated:

python app.py

The application will launch locally at http://127.0.0.1:7860/.

Usage

Tabs Available:

• Project Info: Displays team information and a PDF report.
• Production Planning (PL): Solve and visualise a linear programming problem using product and resource data.
• Staff Scheduling (PLNE): Simulated assignment of employees to shifts based on availability.

Notes

• Visualisations are generated with matplotlib.
• UI built with Gradio Blocks using tabbed layout.
• PDF report embedded with base64 encoding.