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Nonparametric adaptive age replacement with a one-cycle criterion

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  • Coolen-Schrijner, P.
  • Coolen, F.P.A.

Abstract

Age replacement of technical units has received much attention in the reliability literature over the last four decades. Mostly, the failure time distribution for the units is assumed to be known, and minimal costs per unit of time is used as optimality criterion, where renewal reward theory simplifies the mathematics involved but requires the assumption that the same process and replacement strategy continues over a very large (‘infinite’) period of time. Recently, there has been increasing attention to adaptive strategies for age replacement, taking into account the information from the process. Although renewal reward theory can still be used to provide an intuitively and mathematically attractive optimality criterion, it is more logical to use minimal costs per unit of time over a single cycle as optimality criterion for adaptive age replacement. In this paper, we first show that in the classical age replacement setting, with known failure time distribution with increasing hazard rate, the one-cycle criterion leads to earlier replacement than the renewal reward criterion. Thereafter, we present adaptive age replacement with a one-cycle criterion within the nonparametric predictive inferential framework. We study the performance of this approach via simulations, which are also used for comparisons with the use of the renewal reward criterion within the same statistical framework.

Suggested Citation

  • Coolen-Schrijner, P. & Coolen, F.P.A., 2007. "Nonparametric adaptive age replacement with a one-cycle criterion," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 74-84.
  • Handle: RePEc:eee:reensy:v:92:y:2007:i:1:p:74-84
    DOI: 10.1016/j.ress.2005.11.002
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    References listed on IDEAS

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    1. P Coolen-Schrijner & F P A Coolen, 2004. "Adaptive age replacement strategies based on nonparametric predictive inference," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1281-1297, December.
    2. J. Ansell & A. Bendell & S. Humble, 1984. "Age Replacement Under Alternative Cost Criteria," Management Science, INFORMS, vol. 30(3), pages 358-367, March.
    3. P Coolen-Schrijner & F P A Coolen & S C Shaw, 2006. "Nonparametric adaptive opportunity-based age replacement strategies," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 63-81, January.
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    Citations

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    Cited by:

    1. I M MacPhee & F P A Coolen & A M Aboalkhair, 2009. "Nonparametric predictive system reliability with redundancy allocation following component testing," Journal of Risk and Reliability, , vol. 223(3), pages 181-188, September.
    2. T F Lipi & J-H Lim & M J Zuo & W Wang, 2012. "A condition- and age-based replacement model using delay time modelling," Journal of Risk and Reliability, , vol. 226(2), pages 221-233, April.
    3. Dursun, İpek & Akçay, Alp & van Houtum, Geert-Jan, 2022. "Age-based maintenance under population heterogeneity: Optimal exploration and exploitation," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1007-1020.
    4. Zhao, Xufeng & Al-Khalifa, Khalifa N. & Magid Hamouda, Abdel & Nakagawa, Toshio, 2017. "Age replacement models: A summary with new perspectives and methods," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 95-105.
    5. P Coolen-Schrijner & S C Shaw & F P A Coolen, 2009. "Opportunity-based age replacement with a one-cycle criterion," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(10), pages 1428-1438, October.
    6. Hamidi, Maryam & Szidarovszky, Ferenc & Szidarovszky, Miklos, 2016. "New one cycle criteria for optimizing preventive replacement policies," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 42-48.
    7. Truong Ba, H. & Cholette, M.E. & Borghesani, P. & Zhou, Y. & Ma, L., 2017. "Opportunistic maintenance considering non-homogenous opportunity arrivals and stochastic opportunity durations," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 151-161.
    8. 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).
    9. Wu, Shaomin & Coolen, Frank P.A., 2013. "A cost-based importance measure for system components: An extension of the Birnbaum importance," European Journal of Operational Research, Elsevier, vol. 225(1), pages 189-195.
    10. de Jonge, Bram & Dijkstra, Arjan S. & Romeijnders, Ward, 2015. "Cost benefits of postponing time-based maintenance under lifetime distribution uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 15-21.
    11. Zheng, Junjun & Okamura, Hiroyuki & Dohi, Tadashi, 2021. "Age replacement with Markovian opportunity process," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. D Venkat & F P A Coolen & P Coolen-Schrijner, 2010. "Extended opportunity-based age replacement with a one-cycle criterion," Journal of Risk and Reliability, , vol. 224(1), pages 55-62, March.
    13. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    14. Jiang, R., 2018. "Performance evaluation of seven optimization models of age replacement policy," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 302-311.

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