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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 11
3247-3253

Multi-action deep reinforcement learning based scheduling method for spinning machine manufacturing shop floor

Ji Zhiyong1
Yuan Yiping1
Ba Zhiyong1
Fan Panpan1
Tian Fang2
1. School of Mechanical Engineering, Xinjiang University, Urumqi 830000, China
2. Urumqi Technology Innovation R&D & Science & Technology Achievement Transformation Center, Urumqi 830000, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0134

Abstract

The spinning machine manufacturing shop scheduling problem is a flexible Job-Shop scheduling problem with complex process constraints and sequence-dependent setup times. This paper proposed a multi-action deep reinforcement learning algorithm with the optimization objective of minimizing the maximum completion time to ensure the quality of the scheduling solution and improve the on-time order delivery capability of the enterprise. Firstly, this paper modeled the scheduling problem as a multi-Markov decision process. Then, in order to predict the probability distribution of selecting different processes and machines, this paper designed two encoders for the two sub-problems of workpiece selection and machine selection of spinning machine manufacturing plant scheduling, for defining the process selection policy and machine selection policy, respectively. In the process selection encoder, it used a graphical neural network to encode the disjunctive graph to reduce the impact of problem size on the quality of the solution. Based on this, the paper designed a reinforcement learning training algorithm with multiple action spaces for the two substrategies. Finally, it validated the proposed method on a real production case of a spinning machine manufacturing company. The results show that the method exhibits good performance on problems of different scales, is able to obtain higher quality scheduling solutions compared with other comparative algorithms, and the model has better generalization ability and stability.

Foundation Support

国家自然科学基金资助项目(71961029)
新疆维吾尔自治区重点研发计划资助项目(2020B02013)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0134
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 11
Section: Algorithm Research & Explore
Pages: 3247-3253
Serial Number: 1001-3695(2023)11-007-3247-07

Publish History

[2023-07-05] Accepted Paper
[2023-11-05] Printed Article

Cite This Article

纪志勇, 袁逸萍, 巴智勇, 等. 基于多动作深度强化学习的纺机制造车间调度方法 [J]. 计算机应用研究, 2023, 40 (11): 3247-3253. (Ji Zhiyong, Yuan Yiping, Ba Zhiyong, et al. Multi-action deep reinforcement learning based scheduling method for spinning machine manufacturing shop floor [J]. Application Research of Computers, 2023, 40 (11): 3247-3253. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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