Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (23): 248-254.DOI: 10.3778/j.issn.1002-8331.2105-0299

• Engineering and Applications • Previous Articles     Next Articles

Job Shop Scheduling Problem Based on Deep Reinforcement Learning

LI Baoshuai, YE Chunming   

  1. Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
  • Online:2021-12-01 Published:2021-12-02



  1. 上海理工大学 管理学院,上海 200093


This paper proposes a method to deal with the changeable scheduling environment. This method combines the learning ability of deep neural network with the decision-making ability of reinforcement learning. The approach regards the job shop scheduling problem as a sequential decision-making problem. Deep neural network fits the value function. Scheduling state is represented as a matrix form for input. Some of scheduling rules are used as the action space to directly select the behavior strategy. It sets the reward function related to machine utilization, interacts with the environment to obtain the best scheduling rules for each decision point. The results on the OR-Library show the effectiveness of the algorithm.

Key words: reinforcement learning, deep reinforcement learning, job shop scheduling, deep Q network



关键词: 强化学习, 深度强化学习, 作业车间调度, 深度Q网络