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Research on flexible job shop scheduling problem with AGV using double DQN

Author

Listed:
  • Minghai Yuan

    (Hohai University
    Hohai University)

  • Liang Zheng

    (Hohai University
    Hohai University)

  • Hanyu Huang

    (Hohai University
    Hohai University)

  • Kaiwen Zhou

    (Hohai University
    Hohai University)

  • Fengque Pei

    (Hohai University
    Hohai University)

  • Wenbin Gu

    (Hohai University
    Hohai University)

Abstract

In the context of Industry 4.0 and intelligent manufacturing, AGVs are widely used in flexible job shop resource transportation, which sharply increases the uncertainty and complexity of the scheduling process. For this reason, an improved double Deep Q Network (DDQN) real-time scheduling method is proposed for the Flexible Job Shop Scheduling Problem with Automated Guided Vehicle (FJSP-AGV) to minimize the makespan. Firstly, the optimization model of the FJSP-AGV is established, and the corresponding constraints and the objective function are defined. Then, the FJSP-AGV is converted into a Markov Decision Process (MDP), in which the state space, action space, and reward function are defined in detail. Next, an improved DDQN is proposed to generate the optimal scheduling policy considering AGV. Finally, the computational experiments are conducted based on data from public benchmarks and the real-world flexible job shop, and the results demonstrate the accuracy and effectiveness of the proposed algorithm.

Suggested Citation

  • Minghai Yuan & Liang Zheng & Hanyu Huang & Kaiwen Zhou & Fengque Pei & Wenbin Gu, 2025. "Research on flexible job shop scheduling problem with AGV using double DQN," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 509-535, January.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02252-8
    DOI: 10.1007/s10845-023-02252-8
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    References listed on IDEAS

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    1. Weibo Ren & Yan Yan & Yaoguang Hu & Yu Guan, 2022. "Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints," International Journal of Production Research, Taylor & Francis Journals, vol. 60(18), pages 5675-5696, September.
    2. 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.
    3. Ümit Bilge & Gündüz Ulusoy, 1995. "A Time Window Approach to Simultaneous Scheduling of Machines and Material Handling System in an FMS," Operations Research, INFORMS, vol. 43(6), pages 1058-1070, December.
    4. Dalila B. M. M. Fontes & Seyed Mahdi Homayouni, 2019. "Joint production and transportation scheduling in flexible manufacturing systems," Journal of Global Optimization, Springer, vol. 74(4), pages 879-908, August.
    5. Jianxun Li & Wenjie Cheng & Kin Keung Lai & Bhagwat Ram, 2022. "Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging," Mathematics, MDPI, vol. 10(19), pages 1-15, September.
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    Cited by:

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