A Reinforcement Learning Environment For Job-Shop Scheduling

This folder contains the implementation of the paper “A Reinforcement Learning Environment For Job-Shop Scheduling”.

It contains the deep reinforcement learning approach we have developed to solve the Job-Shop Scheduling problem.

The optimized environment is available as a separate repository.

If you’ve found our work useful for your research, you can cite the paper as follows:

@misc{tassel2021reinforcement,
      title={A Reinforcement Learning Environment For Job-Shop Scheduling}, 
      author={Pierre Tassel and Martin Gebser and Konstantin Schekotihin},
      year={2021},
      eprint={2104.03760},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Getting Started

This code has been tested on Ubuntu 18.04 and MacOs 10.15.
Some users have reported difficulties running this program on Windows.

This work uses Ray’s RLLib, Tensorflow and Wandb.

Make sure you have git, cmake, zlib1g, and, on Linux, zlib1g-dev installed.

You also need to have a Weight and Bias account to log your metrics.
Otherwise, just remove all occurrence of wandb and log the metrics in another way.

git clone https://github.com/prosysscience/JSS
cd JSS
pip install -r requirements.txt

Important: Your instance must follow Taillard’s specification.

Project Organization

├── README.md                 <- The top-level README for developers using this project.
└── JSS
    ├── dispatching_rules/      <- Contains the code to run the disptaching rule FIFO and MWTR.
    ├── instances/              <- All Taillard's instances + 5 Demirkol instances.
    ├── randomLoop/             <- A random loop with action mask, usefull to debug environment and
    |                             to check if our agent learn.
    ├── CP.py                   <- OR-Tool's cp model for the JSS problem.
    ├── CustomCallbacks.py      <- A special RLLib's callback used to save the best solution found.
    ├── default_config.py       <- default config used for the disptaching rules.
    ├── env_wrapper.py          <- Envrionment wrapper to save the action's of the best solution found
    ├── main.py                 <- PPO approach, the main file to call to reproduce our approach.
    └── models.py               <- Tensorflow model who mask logits of illegal actions.

License

MIT License