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An accurate approximate solution of optimal sequential age replacement policy for a finite-time horizon

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  • Jiang, R.

Abstract

It is difficult to find the optimal solution of the sequential age replacement policy for a finite-time horizon. This paper presents an accurate approximation to find an approximate optimal solution of the sequential replacement policy. The proposed approximation is computationally simple and suitable for any failure distribution. Their accuracy is illustrated by two examples. Based on the approximate solution, an approximate estimate for the total cost is derived.

Suggested Citation

  • Jiang, R., 2009. "An accurate approximate solution of optimal sequential age replacement policy for a finite-time horizon," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1245-1250.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:8:p:1245-1250
    DOI: 10.1016/j.ress.2009.01.003
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    References listed on IDEAS

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    1. Jiang, R., 2008. "A Gamma–normal series truncation approximation for computing the Weibull renewal function," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 616-626.
    2. Nakagawa, T. & Mizutani, S., 2009. "A summary of maintenance policies for a finite interval," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 89-96.
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    Citations

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    Cited by:

    1. 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.
    2. Zhao, Xufeng & Al-Khalifa, Khalifa N. & Nakagawa, Toshio, 2015. "Approximate methods for optimal replacement, maintenance, and inspection policies," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 68-73.
    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. Cheng, Tianjin & Pandey, Mahesh D. & van der Weide, J.A.M., 2012. "The probability distribution of maintenance cost of a system affected by the gamma process of degradation: Finite time solution," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 65-76.
    5. A Ponchet & M Fouladirad & A Grall, 2011. "Maintenance policy on a finite time span for a gradually deteriorating system with imperfect improvements," Journal of Risk and Reliability, , vol. 225(2), pages 105-116, June.
    6. 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.
    7. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    8. Charles Wells, 2015. "Bounds on uptime distribution based on aging for systems with finite lifetimes," Annals of Operations Research, Springer, vol. 235(1), pages 757-769, December.
    9. 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.
    10. Jiang, R., 2010. "A simple approximation for the renewal function with an increasing failure rate," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 963-969.

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