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Flexible Job Shop Dynamic Scheduling and Fault Maintenance Personnel Cooperative Scheduling Optimization Based on the ACODDQN Algorithm

Author

Listed:
  • Jiansha Lu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jiarui Zhang

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jun Cao

    (Haitian Plastics Machinery Group Limited Company, Ningbo 315801, China)

  • Xuesong Xu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yiping Shao

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
    Ningbo Yongxin Optics Co., Ltd., Ningbo 315040, China)

  • Zhenbo Cheng

    (College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

In order to address the impact of equipment fault diagnosis and repair delays on production schedule execution in the dynamic scheduling of flexible job shops, this paper proposes a multi-resource, multi-objective dynamic scheduling optimization model, which aims to minimize delay time and completion time. It integrates the scheduling of the workpieces, machines, and maintenance personnel to improve the response efficiency of emergency equipment maintenance. To this end, a self-learning Ant Colony Algorithm based on deep reinforcement learning (ACODDQN) is designed in this paper. The algorithm searches the solution space by using the ACO, prioritizes the solutions by combining the non-dominated sorting strategies, and achieves the adaptive optimization of scheduling decisions by utilizing the organic integration of the pheromone update mechanism and the DDQN framework. Further, the generated solutions are locally adjusted via the feasible solution optimization strategy to ensure that the solutions satisfy all the constraints and ultimately generate a Pareto optimal solution set with high quality. Simulation results based on standard examples and real cases show that the ACODDQN algorithm exhibits significant optimization effects in several tests, which verifies its superiority and practical application potential in dynamic scheduling problems.

Suggested Citation

  • Jiansha Lu & Jiarui Zhang & Jun Cao & Xuesong Xu & Yiping Shao & Zhenbo Cheng, 2025. "Flexible Job Shop Dynamic Scheduling and Fault Maintenance Personnel Cooperative Scheduling Optimization Based on the ACODDQN Algorithm," Mathematics, MDPI, vol. 13(6), pages 1-27, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:932-:d:1610136
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    References listed on IDEAS

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    1. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
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