Job-Shop Scheduling Environment

An optimized OpenAi gym's environment to simulate the Job-Shop Scheduling problem.

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 repository is available as a pip package:

pip install JSSEnv

Once installed, the environment will be available in your OpenAi's gym environment and can be used to train a reinforcement learning agent:

import gym
import JSSEnv # an ongoing issue with OpenAi's gym causes it to not import automatically external modules, see: https://github.com/openai/gym/issues/2809
# for older version of gym, you have to use 
# env = gym.make('JSSEnv:jss-v1', env_config={'instance_path': 'INSTANCE_PATH'})
env = gym.make('jss-v1', env_config={'instance_path': 'INSTANCE_PATH'})

Important: Your instance must follow Taillard's specification.

How To Use

The observation provided by the environment contains both a boolean array indicating if the action is legal or not and the "real" observation

self.observation_space = gym.spaces.Dict({
            "action_mask": gym.spaces.Box(0, 1, shape=(self.jobs + 1,)),
            "real_obs": gym.spaces.Box(low=0.0, high=1.0, shape=(self.jobs, 7), dtype=np.float),
        })

A random agent would have to sample legal action from this action_mask array, otherwise, you might take illegal actions.
In theory, it is not possible to take the same action over and over again as the job will have one of his operations currently on a machine and might not be free for schedule.

For research purposes, I've made a random loop using RLLib: https://github.com/prosysscience/RL-Job-Shop-Scheduling/blob/0bbe0c0f2b8a742b75cbe67c5f6a825b8cfdf5eb/JSS/randomLoop/random_loop.py

If you don't want to use RLLib, you can write a simple random loop using numpy.random.choice function:

import numpy as np
np.random.choice(len(legal_action), 1, p=(legal_action / legal_action.sum()))[0]

Where legal_action is the array of legal action (i.e., action_mask).
This line of code will randomly sample one legal action from the action_mask.

Project Organization

├── README.md             <- The top-level README for developers using this project.
├── JSSEnv
│   └── envs              <- Contains the environment.
│       └── instances     <- Contains some intances from the litterature.
│
└── tests                 
    │
    ├── test_state.py     <- Unit tests focus on testing the state produced by
    │                        the environment.
    │
    ├── test_rendering.py <- Unit tests for the rendering, mainly used as an example
    |                        how to render the environment.
    │
    └── test_solutions.py <- Unit tests to ensure that our environment is correct checking
                             known solution in the litterature leads to the intended make-
                             span. We also check if all actions provided by the solution are
                             legal in our environment.

License

MIT License

inyunch / JSSEnv