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Calendar-based age replacement policy with dependent renewal cycles

Author

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  • Maliheh Aramon Bajestani
  • Dragan Banjevic

Abstract

In this article, we introduce an age-based replacement policy in which the preventive replacements are restricted to specific calendar times. Under the new policy, the assets are renewed at failure or if their ages are greater than or equal to a replacement age at given calendar times, whichever occurs first. This policy is logistically applicable in industries such as utilities where there are large and geographically diverse populations of deteriorating assets with different installation times. Since preventive replacements are performed at fixed times, the renewal cycles are dependent random variables. Therefore, the classic renewal reward theorem cannot be directly applied. Using the theory of Markov chains with general state space and a suitably defined ergodic measure, we analyze the problem to find the optimal replacement age, minimizing the long-run expected cost per time unit. We further find the limiting distributions of the backward and forward recurrence times for this policy and show how our ergodic measure can be used to analyze more complicated policies. Finally, using a real data set of utility wood poles’ maintenance records, we numerically illustrate some of our results including the importance of defining an appropriate ergodic measure in reducing the computational expense.

Suggested Citation

  • Maliheh Aramon Bajestani & Dragan Banjevic, 2016. "Calendar-based age replacement policy with dependent renewal cycles," IISE Transactions, Taylor & Francis Journals, vol. 48(11), pages 1016-1026, November.
  • Handle: RePEc:taf:uiiexx:v:48:y:2016:i:11:p:1016-1026
    DOI: 10.1080/0740817X.2016.1163444
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    Cited by:

    1. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    2. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Jun Wang & Yuyang Wang & Yuqiang Fu, 2023. "Joint Optimization of Condition-Based Maintenance and Performance Control for Linear Multi-State Consecutively Connected Systems," Mathematics, MDPI, vol. 11(12), pages 1-19, June.
    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. Yonit Barron & Uri Yechiali, 2017. "Generalized control-limit preventive repair policies for deteriorating cold and warm standby Markovian systems," IISE Transactions, Taylor & Francis Journals, vol. 49(11), pages 1031-1049, November.
    6. Zhang, Fengxia & Liao, Haitao & Shen, Jingyuan & Ma, Yizhong, 2024. "Optimal maintenance over a finite time horizon for a system under imperfect inspection and dynamic working environment," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    7. Sheu, Shey-Huei & Tsai, Hsin-Nan & Sheu, Uan-Yu & Zhang, Zhe George, 2019. "Optimal replacement policies for a system based on a one-cycle criterion," Reliability Engineering and System Safety, Elsevier, vol. 191(C).

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