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Joint condition-based maintenance and spare provisioning policy for a K-out-of-N system with failures during inspection intervals

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  • Zheng, Meimei
  • Lin, Jie
  • Xia, Tangbin
  • Liu, Yu
  • Pan, Ershun

Abstract

Recent advances in dedicated sensors and surveillance technology have given rise to condition-based maintenance. In addition to the degradation condition of the system, maintenance schedules should take into account the availability of spare parts. By incorporating spare part order information, planners can make more precise arrangements for both maintenance and spare parts replenishment, leading to a reduction in operating cost. This paper investigates the joint optimization of condition-based maintenance and spare provisioning for a K-out-of-N system considering failures during inspection intervals. The degradation states of the system are revealed at periodic inspection epochs where planners have the opportunity to replace components and order spare parts. The resulting sequential decision problem is formulated as a Markov decision process that integrates the comprehensive information of the components and spare parts orders into decision making. We propose an optimal joint policy by using the value iteration algorithm and compare it with two separated policies that consider part of the information. Sensitivity experiments are conducted to analyze the impact of the parameters on the effect of the proposed policy with the comprehensive information. The results show that the comprehensive information is more important to the ordering decisions than the replacing decisions.

Suggested Citation

  • Zheng, Meimei & Lin, Jie & Xia, Tangbin & Liu, Yu & Pan, Ershun, 2023. "Joint condition-based maintenance and spare provisioning policy for a K-out-of-N system with failures during inspection intervals," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1220-1232.
    • Handle: RePEc:eee:ejores:v:308:y:2023:i:3:p:1220-1232
    DOI: 10.1016/j.ejor.2023.01.011
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    References listed on IDEAS

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    3. Karabağ, Oktay & Bulut, Önder & Toy, Ayhan Özgür & Fadıloğlu, Mehmet Murat, 2024. "An efficient procedure for optimal maintenance intervention in partially observable multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. 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.
    5. Sun, Mingyao & Ng, Chi To & Yang, Liu & Zhang, Tianhua, 2024. "Optimal after-sales service offering strategy: Additive manufacturing, traditional manufacturing, or hybrid?," International Journal of Production Economics, Elsevier, vol. 268(C).

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