Chinese Journal on Internet of Things ›› 2022, Vol. 6 ›› Issue (1): 53-64.doi: 10.11959/j.issn.2096-3750.2022.00260

• Theory and Technology • Previous Articles     Next Articles

Research on deep reinforcement learning based intelligent shop scheduling method

Zihui LUO, Chengling JIANG, Liang LIU, Xiaolong ZHENG, Huadong MA   

  1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Revised:2022-01-21 Online:2022-03-30 Published:2022-03-01
  • Supported by:
    The National Natural Science Foundation of China(62061146002);The National Natural Science Foundation of China(61632008);The National Natural Science Foundation of China(61921003);The Fundamental Research Funds for the Central Universities(2019XD-A14)


The unprecedented prosperity of the industrial internet of things (IIoT) has opened up a new path for the traditional industrial manufacturing model.Intelligent shop scheduling is one of the key technologies to achieve the overall control and flexible production of the whole production process.It requires an effective plan with a minimum makespan to allocate multiple processes and multiple machines for production scheduling.Firstly, the shop scheduling problem was defined as a Markov decision process (MDP), and a shop scheduling model based on the pointer network was established.Secondly, the job scheduling process was regarded as a mapping from one sequence to another, and a new shop scheduling algorithm based on deep reinforcement learning (DRL) was proposed.By analyzing the convergence of the model under different parameter settings, the optimal parameters were determined.Experimental results on different scales of public data sets and actual production data sets show that the proposed DRL algorithm can obtain better performances.

Key words: IIoT, intelligent shop scheduling, flexible production, deep reinforcement learning, shop scheduling method

CLC Number: 

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