Ant-Colony-Optimization

Ant Colony Optimization (ACO) for Job Shop Scheduling Problem.

Execution:

Initial requirements:

• Pytho 3.8.2
• Venv (sudo apt install python3.8-venv)
• pip (sudo apt install python3-pip)

Initial setup:

1. Create enviroment: python3 -m venv ./code/venv
2. Activate enviroment: cd code && source venv/bin/activate
3. Install requirements: pip install -r requirements.txt

Execute:

1. Activate enviroment: cd code && source venv/bin/activate
2. Execute: python3 main.py
3. Exit: deactivate

Input:

The optimized parameters are at the beginning of the main file, change at will.

Output:

When executing the algorithm, the time of the best schedule will be printed. A ACO_cycles_results.json file will also be generated, where all time results per cycles will be recorded with the following order: the fastest, the average and the longest time.

Data:

The test instances used for exeperiments on documentation are in /test_instances.

These instances are from a contribution to the OR-Library by Dirk C. Mattfeld and Rob J.M. Vaessens

References:

• FLÓREZ, Edson; GÓMEZ, Wilfredo; BAUTIST, Lola. An ant colony optimization algorithm for job shop scheduling problem. Universidad Industrial de Santander, Bucaramanga, Colombia. arXiv:1309.5110, 2013

• COLORNI, A.; DORIGO, M.; MANIEZZO, V.; TRUBIAN, M. Ant system for Job-shop scheduling. JORBEL - Belgian Journal of Operations Research, Statistics, and Computer Science, v. 34, n. 1, p. 39-53, 1 jan. 1995.