Reinforcement Learning for Job Shop Scheduling Problem
Operations Research
Machine Learning
Ho et al. (2024), Park et al. (2021), Yuan et al. (2024)
gifflar and thompson algorithm
taillard benchmark
References
Ho, Kuo-Hao, Jui-Yu Cheng, Ji-Han Wu, et al. 2024. “Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem.” IEEE Access: Practical Innovations, Open Solutions 12: 14703–18. https://doi.org/10.1109/access.2024.3357969.
Park, Junyoung, Sanjar Bakhtiyar, and Jinkyoo Park. 2021. “ScheduleNet: Learn to Solve Multi-Agent Scheduling Problems with Reinforcement Learning.” arXiv [Cs.LG], ahead of print. https://doi.org/10.48550/arXiv.2106.03051.
Yuan, Erdong, Liejun Wang, Shuli Cheng, Shiji Song, Wei Fan, and Yongming Li. 2024. “Solving Flexible Job Shop Scheduling Problems via Deep Reinforcement Learning.” Expert Systems With Applications 245 (123019): 123019. https://doi.org/10.1016/j.eswa.2023.123019.