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Optimal age-replacement time with minimal repair based on cumulative repair-cost limit for a system subject to shocks

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  • Shey-Huei Sheu
  • Chin-Chih Chang
  • Yu-Hung Chien

Abstract

An operating system is subject to random shocks that arrive according to a non-homogeneous Poisson process and cause the system failed. System failures experience to be divided into two categories: a type-I failure (minor), rectified by a minimal repair; or a type-II failure (catastrophic) that calls for a replacement. An age-replacement model is studied by considering both a cumulative repair-cost limit and a system’s entire repair-cost history. Under such a policy, the system is replaced at age T, or at the k-th type-I failure at which the accumulated repair cost exceeds the pre-determined limit, or at any type-II failure, whichever occurs first. The object of this article is to study analytically the minimum-cost replacement policy for showing its existence, uniqueness, and the structural properties. The proposed model provides a general framework for analyzing the maintenance policies, and presents several numerical examples for illustration purposes. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Shey-Huei Sheu & Chin-Chih Chang & Yu-Hung Chien, 2011. "Optimal age-replacement time with minimal repair based on cumulative repair-cost limit for a system subject to shocks," Annals of Operations Research, Springer, vol. 186(1), pages 317-329, June.
    • Handle: RePEc:spr:annopr:v:186:y:2011:i:1:p:317-329:10.1007/s10479-011-0864-9
    DOI: 10.1007/s10479-011-0864-9
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    References listed on IDEAS

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    Cited by:

    1. Caiyun Niu & Xiaolin Liang & Bingfeng Ge & Xue Tian & Yingwu Chen, 2016. "Optimal replacement policy for a repairable system with deterioration based on a renewal-geometric process," Annals of Operations Research, Springer, vol. 244(1), pages 49-66, September.
    2. Dai, Anshu & Wang, Xin & Li, Yu & Li, Ting & He, Shuguang, 2023. "Design of a performance-based warranty policy with replacement–repair strategy and cumulative cost threshold," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Chien, Yu-Hung & Sheu, Shey-Huei & Zhang, Zhe George, 2012. "Optimal maintenance policy for a system subject to damage in a discrete time process," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 1-10.
    4. Xufeng Zhao & Toshio Nakagawa, 2016. "Over-time and over-level replacement policies with random working cycles," Annals of Operations Research, Springer, vol. 244(1), pages 103-116, September.
    5. Reza Ahmadi, 2014. "Optimal maintenance scheduling for a complex manufacturing system subject to deterioration," Annals of Operations Research, Springer, vol. 217(1), pages 1-29, June.
    6. Natali Hritonenko & Yuri Yatsenko, 2012. "Fleet replacement under technological shocks," Annals of Operations Research, Springer, vol. 196(1), pages 311-331, July.
    7. Jian-Ming Bai & Zhe Zhang & Ze-Hui Li, 2014. "Lifetime properties of a cumulative shock model with a cluster structure," Annals of Operations Research, Springer, vol. 212(1), pages 21-41, January.
    8. Zhao, Xufeng & Qian, Cunhua & Nakagawa, Toshio, 2013. "Optimal policies for cumulative damage models with maintenance last and first," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 50-59.
    9. Fei Zhao & Fengfeng Xie & Chenghua Shi & Jianshe Kang, 2017. "A Joint Inspection-Based Preventive Maintenance and Spare Ordering Optimization Policy Using a Three-Stage Failure Process," Complexity, Hindawi, vol. 2017, pages 1-19, November.
    10. Yonit Barron, 2018. "Group maintenance policies for an R-out-of-N system with phase-type distribution," Annals of Operations Research, Springer, vol. 261(1), pages 79-105, February.

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