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Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system

Author

Listed:
  • Xiao Wang

    (Huazhong University of Science and Technology
    Shenyang Aerospace University)

  • Hongwei Wang

    (Huazhong University of Science and Technology)

  • Chao Qi

    (Huazhong University of Science and Technology)

Abstract

This paper investigates the maintenance problem for a flow line system consisting of two series machines with an intermediate finite buffer in between. Both machines independently deteriorate as they operate, resulting in multiple yield levels. Resource constrained imperfect preventive maintenance actions may bring the machine back to a better state. The problem is modeled as a semi-Markov decision process. A distributed multi-agent reinforcement learning algorithm is proposed to solve the problem and to obtain the control-limit maintenance policy for each machine associated with the observed state represented by yield level and buffer level. An asynchronous updating rule is used in the learning process since the state transitions of both machines are not synchronous. Experimental study is conducted to evaluate the efficiency of the proposed algorithm.

Suggested Citation

  • Xiao Wang & Hongwei Wang & Chao Qi, 2016. "Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 325-333, April.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:2:d:10.1007_s10845-013-0864-5
    DOI: 10.1007/s10845-013-0864-5
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    References listed on IDEAS

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    Cited by:

    1. Ye, Zhenggeng & Cai, Zhiqiang & Yang, Hui & Si, Shubin & Zhou, Fuli, 2023. "Joint optimization of maintenance and quality inspection for manufacturing networks based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    2. Wei, Shuaichong & Nourelfath, Mustapha & Nahas, Nabil, 2023. "Analysis of a production line subject to degradation and preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Orestes Llanes Santiago & Luis Anido Rifón, 2018. "Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1941-1952, December.
    4. Yang, Hongbing & Li, Wenchao & Wang, Bin, 2021. "Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    5. Zheng, Meimei & Su, Zhiyun & Wang, Dong & Pan, Ershun, 2024. "Joint maintenance and spare part ordering from multiple suppliers for multicomponent systems using a deep reinforcement learning algorithm," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Qinming Liu & Ming Dong & Wenyuan Lv & Chunming Ye, 2019. "Manufacturing system maintenance based on dynamic programming model with prognostics information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1155-1173, March.
    7. Jorge Ribeiro & Pedro Andrade & Manuel Carvalho & Catarina Silva & Bernardete Ribeiro & Licínio Roque, 2022. "Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation," Mathematics, MDPI, vol. 10(9), pages 1-20, May.
    8. Johannes Dornheim & Lukas Morand & Samuel Zeitvogel & Tarek Iraki & Norbert Link & Dirk Helm, 2022. "Deep reinforcement learning methods for structure-guided processing path optimization," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 333-352, January.
    9. Zhang, Ning & Qi, Faqun & Zhang, Chengjie & Zhou, Hongming, 2022. "Joint optimization of condition-based maintenance policy and buffer capacity for a two-unit series system," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    10. Mohammadi, Reza & He, Qing, 2022. "A deep reinforcement learning approach for rail renewal and maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Yuanju Qu & Zengtao Hou, 2022. "Degradation principle of machines influenced by maintenance," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1521-1530, June.
    12. 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.
    13. Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).

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