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

• Published in: European journal of operational research Vol. 308; no. 3; pp. 1220 - 1232
• Main Authors: Zheng, Meimei, Lin, Jie, Xia, Tangbin, Liu, Yu, Pan, Ershun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2023
• Subjects: K-out-of-N system; Maintenance; Markov decision process; Spare provisioning; Markov decision process; K-out-of-N system; Maintenance; Spare provisioning
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2023.01.011

•A K-out-of-N system with failures during inspection intervals is considered.•The joint condition-based maintenance and spare provisioning are optimized.•The optimal policy is compared with two policies with part of the information.•The number of components to be replaced may decrease with the degradation values.•The joint information is more useful for ordering decisions than replacing ones. 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.