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Revisiting discrete time age replacement policy for phase-type lifetime distributions

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  • Eryilmaz, Serkan

Abstract

For a system (or unit) whose lifetime is measured by the number cycles, according to the discrete time age replacement policy, it is replaced preventively after n cycles or correctively at failure, whichever occurs first. In this paper, discrete time age replacement policy is revisited when the lifetime of the system is modeled by a discrete phase-type distribution. In particular, the necessary conditions for the unique and finite replacement cycle which minimizes the expected cost per unit of time are obtained. The necessary conditions are mainly based on the behavior of the hazard rate. The results are illustrated for some special discrete phase-type lifetime distributions. Computational results are also presented for the optimal replacement cycle under specific real life setups.

Suggested Citation

  • Eryilmaz, Serkan, 2021. "Revisiting discrete time age replacement policy for phase-type lifetime distributions," European Journal of Operational Research, Elsevier, vol. 295(2), pages 699-704.
    • Handle: RePEc:eee:ejores:v:295:y:2021:i:2:p:699-704
    DOI: 10.1016/j.ejor.2021.03.007
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    References listed on IDEAS

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    1. Markos V. Koutras & Serkan Eryilmaz, 2017. "Compound Geometric Distribution of Order k," Methodology and Computing in Applied Probability, Springer, vol. 19(2), pages 377-393, June.
    2. Chien, Yu-Hung, 2008. "A general age-replacement model with minimal repair under renewing free-replacement warranty," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1046-1058, May.
    3. Fouladirad, Mitra & Paroissin, Christian & Grall, Antoine, 2018. "Sensitivity of optimal replacement policies to lifetime parameter estimates," European Journal of Operational Research, Elsevier, vol. 266(3), pages 963-975.
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    5. Navarro, Jorge & Arriaza, Antonio & Suárez-Llorens, Alfonso, 2019. "Minimal repair of failed components in coherent systems," European Journal of Operational Research, Elsevier, vol. 279(3), pages 951-964.
    6. Zhao, Xufeng & Mizutani, Satoshi & Nakagawa, Toshio, 2015. "Which is better for replacement policies with continuous or discrete scheduled times?," European Journal of Operational Research, Elsevier, vol. 242(2), pages 477-486.
    7. 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.
    8. Levitin, Gregory & Finkelstein, Maxim & Dai, Yuanshun, 2018. "Heterogeneous standby systems with shocks-driven preventive replacements," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1189-1197.
    9. Safaei, Fatemeh & Châtelet, Eric & Ahmadi, Jafar, 2020. "Optimal age replacement policy for parallel and series systems with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    10. Eisele, Karl-Theodor, 2006. "Recursions for compound phase distributions," Insurance: Mathematics and Economics, Elsevier, vol. 38(1), pages 149-156, February.
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    Cited by:

    1. Eryilmaz, Serkan & Yalcin, Femin, 2022. "The number of failed components upon system failure when the lifetimes are discretely distributed," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Wu, Jing & Qian, Cunhua & Dohi, Tadashi, 2024. "Optimal opportunity-based age replacement policies in discrete time," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Serkan Eryilmaz & Fatih Tank, 2023. "Optimal age replacement policy for discrete time parallel systems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 475-490, October.

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