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A Dynamic Scheduling Method Combining Iterative Optimization and Deep Reinforcement Learning to Solve Sudden Disturbance Events in a Flexible Manufacturing Process

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  • Jun Yan

    (Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing 100124, China
    Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China)

  • Tianzuo Zhao

    (Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing 100124, China)

  • Tao Zhang

    (Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China)

  • Hongyan Chu

    (Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing 100124, China)

  • Congbin Yang

    (Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing 100124, China)

  • Yueze Zhang

    (Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing 100124, China
    Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China)

Abstract

Unpredictable sudden disturbances such as machine failure, processing time lag, and order changes increase the deviation between actual production and the planned schedule, seriously affecting production efficiency. This phenomenon is particularly severe in flexible manufacturing. In this paper, a dynamic scheduling method combining iterative optimization and deep reinforcement learning (DRL) is proposed to address the impact of uncertain disturbances. A real-time DRL production environment model is established for the flexible job scheduling problem. Based on the DRL model, an agent training strategy and an autonomous decision-making method are proposed. An event-driven and period-driven hybrid dynamic rescheduling trigger strategy (HDRS) with four judgment mechanisms has been developed. The decision-making method and rescheduling trigger strategy solve the problem of how and when to reschedule for the dynamic scheduling problem. The data experiment results show that the trained DRL decision-making model can provide timely feedback on the adjusted scheduling arrangements for different-scale order problems. The proposed dynamic-scheduling decision-making method and rescheduling trigger strategy can achieve high responsiveness, quick feedback, high quality, and high stability for flexible manufacturing process scheduling decision making under sudden disturbance.

Suggested Citation

  • Jun Yan & Tianzuo Zhao & Tao Zhang & Hongyan Chu & Congbin Yang & Yueze Zhang, 2024. "A Dynamic Scheduling Method Combining Iterative Optimization and Deep Reinforcement Learning to Solve Sudden Disturbance Events in a Flexible Manufacturing Process," Mathematics, MDPI, vol. 13(1), pages 1-28, December.
  • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:4-:d:1551522
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    References listed on IDEAS

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    1. Xiaomei He & Shaohua Dong & Ning Zhao, 2020. "Research on rush order insertion rescheduling problem under hybrid flow shop based on NSGA-III," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1161-1177, February.
    2. Zachariah Stevenson & Ricardo Fukasawa & Luis Ricardez-Sandoval, 2020. "Evaluating periodic rescheduling policies using a rolling horizon framework in an industrial-scale multipurpose plant," Journal of Scheduling, Springer, vol. 23(3), pages 397-410, June.
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