MetaFlowScheduler

A customizable metaheuristic framework for solving multi-stage no-wait flowshop scheduling problems, based on real-world academic research.

🧩 Problem Overview

This project focuses on solving the multi-stage no-wait flexible flowshop scheduling problem (NWFSP), where jobs must pass through several machines in strict sequence, and no job can wait between stages. It's a classical NP-hard scheduling problem, often found in manufacturing, production, and logistics.

🎯 Objective

Minimize the makespan (total completion time) by assigning job sequences optimally across machines, using metaheuristic techniques such as:

• Particle Swarm Optimization (PSO)
• Genetic Algorithms (GA)
• Tabu Search (TS)

🛠 Features

• Modular solver design using the DEAP library
• Simulated benchmark job data
• Plotting of Gantt charts and convergence
• Configurable job instances and machine layouts

📁 Folder Structure

MetaFlowScheduler/
├── data/                # Simulated benchmark job data
├── notebooks/           # EDA, experimentation, and visualization
├── results/             # Logs, convergence plots, Gantt charts
├── src/                 # Core solver implementations
├── requirements.txt     # Python dependencies
└── README.md            # Project documentation

🚀 Getting Started

1. Clone the repo:

git clone https://github.com/mageed-ghaleb/MetaFlowScheduler.git
cd MetaFlowScheduler

2. Install dependencies:

pip install -r requirements.txt

3. Run a solver:

python src/run_pso_solver.py

📊 Visual Output

• Convergence plots of fitness over iterations
• Gantt chart visualizations of job schedules

👨‍💻 Author

Developed by Mageed Ghaleb – Co-Founder of MetaForge | Optimization & AI Specialist
Based on peer-reviewed research in scheduling, metaheuristics, and industrial optimization.

📄 License

MIT License – Free to use with attribution.