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Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning

Author

Listed:
  • Ziqing Wang

    (Chongqing University)

  • Wenzhu Liao

    (Chongqing University)

Abstract

In the era of Industry 4.0, production scheduling as a critical part of manufacturing system should be smarter. Smart scheduling agent is required to be real-time autonomous and possess the ability to face unforeseen and disruptive events. However, traditional methods lack adaptability and intelligence. Hence, this paper is devoted to proposing a smart approach based on proximal policy optimization (PPO) to solve dynamic job shop scheduling problem with random job arrivals. The PPO scheduling agent is trained based on an integration framework of discrete event simulation and deep reinforcement learning. Copies of trained agent can be linked with each machine for distributed control. Meanwhile, state features, actions and rewards are designed for scheduling at each decision point. Reward scaling are applied to improve the convergence performance. The numerical experiments are conducted on cases with different production configurations. The results show that PPO method can realize on-line decision making and provide better solution than dispatch rules and heuristics. It can achieve a balance between time and quality. Moreover, the trained model could also maintain certain performance even in untrained scenarios.

Suggested Citation

  • Ziqing Wang & Wenzhu Liao, 2024. "Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2593-2610, August.
  • Handle: RePEc:spr:joinma:v:35:y:2024:i:6:d:10.1007_s10845-023-02161-w
    DOI: 10.1007/s10845-023-02161-w
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    References listed on IDEAS

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    1. Teofilo Gonzalez & Sartaj Sahni, 1978. "Flowshop and Jobshop Schedules: Complexity and Approximation," Operations Research, INFORMS, vol. 26(1), pages 36-52, February.
    2. Yu-Fang Wang, 2020. "Adaptive job shop scheduling strategy based on weighted Q-learning algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 417-432, February.
    3. Renke Liu & Rajesh Piplani & Carlos Toro, 2022. "Deep reinforcement learning for dynamic scheduling of a flexible job shop," International Journal of Production Research, Taylor & Francis Journals, vol. 60(13), pages 4049-4069, July.
    4. F. Tao & Y. Cheng & L. Zhang & A. Y. C. Nee, 2017. "Advanced manufacturing systems: socialization characteristics and trends," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1079-1094, June.
    5. Andreas Kuhnle & Jan-Philipp Kaiser & Felix Theiß & Nicole Stricker & Gisela Lanza, 2021. "Designing an adaptive production control system using reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 855-876, March.
    6. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    7. Shengluo Yang & Zhigang Xu, 2022. "Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 60(16), pages 4936-4953, August.
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