Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods

Welcome to the Job Shop Scheduling Benchmark

This GitHub repository serves as a comprehensive benchmark for a wide range of machine scheduling problems, including Job Shop Scheduling (JSP), Flow Shop Scheduling (FSP), Flexible Job Shop Scheduling (FJSP), FJSP with Assembly constraints (FAJSP), FJSP with Sequence-Dependent Setup Times (FJSP-SDST), and the online FJSP (with online job arrivals). Our primary goal is to provide a centralized hub for researchers, practitioners, and enthusiasts interested in tackling machine scheduling challenges.

🛠 Solution methods

We aim to include a wide range of solution methods capable of solving machine scheduling problems with various constraints and characteristics. This selection ranges from load-balancing heuristics, dispatching rules and genetic algorithms to end-to-end Deep Reinforcement Learning solutions. The repo currently contains the following solution methods, each capable of solving machine scheduling problems with the corresponding characteristics:

Solution methods	Job Shop (JSP)	Flow Show (FSP)	Flexible Job Shop (FJSP)	FJSP SDST	FAJSP	Online (F)JSP
Dispatching Rules	✓	✓	✓		✓	✓*
Genetic Algorithm	✓	✓	✓	✓	✓
MILP	✓	✓	✓	✓
CP-SAT	✓	✓	✓	✓
FJSP-DRL	✓	✓	✓
L2D	✓	✓
DANIEL	✓	✓	✓

*Capable of online arrivals of FJSP problems

🔜 We have a few DRL-based solutions in the pipeline, which will be published here upon completion.

📢 We encourage you to make use of our repository to get started with your own solutions, and, when possible, release your solution method in this repository.

📝 Cite our repository:

Please consider citing our paper if you use code or ideas from this project:

Robbert Reijnen, Kjell van Straaten, Zaharah Bukhsh, and Yingqian Zhang (2023) Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods. arXiv preprint arXiv:2308.12794

@misc{reijnen2023job,
      title={Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods}, 
      author={Robbert Reijnen and Kjell van Straaten and Zaharah Bukhsh and Yingqian Zhang},
      year={2023},
      eprint={2308.12794},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}