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Maintenance optimisation of a series production system with intermediate buffers using a multi-agent FMDP

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  • Zhou, Yifan
  • Guo, Yiming
  • Lin, Tian Ran
  • Ma, Lin

Abstract

Maintenance decision making of a series production system with intermediate buffers is a research topic of practical significance. The Markov decision process (MDP) is an effective tool to undertake maintenance optimisation of a series production system with buffers due to its capacity in dealing with complex structure of maintenance strategies for such systems. However, the MDP has only been employed to analyse small systems due to the “curse of dimensionality†. A multi-agent factored Markov decision process (FMDP) is adopted in this study to remit the “curse of dimensionality†. The series production system considered in the study consists of several subsystems, and each subsystem is managed by an agent. A new method is developed to select maintenance actions in cooperation of different agents. An approximate linear programming algorithm is used to solve the FMDP model efficiently. The numerical study shows that the developed methods can deal with medium-scale series production systems with an insignificant small error in maintenance decision making.

Suggested Citation

  • Zhou, Yifan & Guo, Yiming & Lin, Tian Ran & Ma, Lin, 2018. "Maintenance optimisation of a series production system with intermediate buffers using a multi-agent FMDP," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 39-48.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:39-48
    DOI: 10.1016/j.ress.2018.07.008
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Barlow, E. & Bedford, T. & Revie, M. & Tan, J. & Walls, L., 2021. "A performance-centred approach to optimising maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 292(2), pages 579-595.
    3. Gao, Kaiye & Peng, Rui & Qu, Li & Wu, Shaomin, 2020. "Jointly optimizing lot sizing and maintenance policy for a production system with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    5. Yifan Zhou & Chao Yuan & Tian Ran Lin & Lin Ma, 2021. "Maintenance policy structure investigation and optimisation of a complex production system with intermediate buffers," Journal of Risk and Reliability, , vol. 235(3), pages 458-473, June.
    6. Zhou, Yifan & Li, Bangcheng & Lin, Tian Ran, 2022. "Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Xu, Jianyu & Liu, Bin & Zhao, Xiujie & Wang, Xiao-Lin, 2024. "Online reinforcement learning for condition-based group maintenance using factored Markov decision processes," European Journal of Operational Research, Elsevier, vol. 315(1), pages 176-190.

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