Brief Review on Applying Reinforcement Learning to Job Shop Scheduling Problems

Authors

Xiaohan Wang, 1. Beihang University, Beijing 100191, China; ;2. Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing 100191, China;
Zhang Lin, 1. Beihang University, Beijing 100191, China; ;2. Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing 100191, China;
Ren Lei, 1. Beihang University, Beijing 100191, China; ;2. Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing 100191, China;
Kunyu Xie, 1. Beihang University, Beijing 100191, China; ;2. Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing 100191, China;
Kunyu Wang, 1. Beihang University, Beijing 100191, China; ;2. Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing 100191, China;
Ye Fei, 1. Beihang University, Beijing 100191, China; ;2. Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing 100191, China;
Chen Zhen, 1. Beihang University, Beijing 100191, China; ;2. Engineering Research Center of Complex Product Advanced Manufacturing Systems, Ministry of Education, Beijing 100191, China;

Abstract

Abstract: Reinforcement Learning (RL) achieves lower time response and better model generalization in Job Shop Scheduling Problem (JSSP). To explain the current overall research status of JSSP based on RL, summarize the current scheduling framework based on RL, and lay the foundation for follow-up research, the backgrounds of JSSP and RL are introduced. Two simulation techniques commonly used in JSSP are analyzed and two commonly used frameworks for RL to solve JSSP are given. In addition, some existing challenges are pointed out, and related research progress is introduced from three aspects: direct scheduling, feature representation-based scheduling, and parameter search-based scheduling.

Recommended Citation

Wang, Xiaohan; Lin, Zhang; Lei, Ren; Xie, Kunyu; Wang, Kunyu; Fei, Ye; and Zhen, Chen (2022) "Brief Review on Applying Reinforcement Learning to Job Shop Scheduling Problems," Journal of System Simulation: Vol. 33: Iss. 12, Article 3.
DOI: 10.16182/j.issn1004731x.joss.21-FZ0774
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss12/3

First Page

2782

Revised Date

2021-07-29

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-FZ0774

Last Page

2791

CLC

TP391.9

Recommended Citation

Wang Xiaohan, Zhang Lin, Ren Lei, Xie Kunyu, Wang Kunyu, Ye Fei, Chen Zhen. Brief Review on Applying Reinforcement Learning to Job Shop Scheduling Problems[J]. Journal of System Simulation, 2021, 33(12): 2782-2791.

DOI

10.16182/j.issn1004731x.joss.21-FZ0774