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Optimal extended warranty length with limited number of repairs in the warranty period

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  • Hooti, Fatemeh
  • Ahmadi, Jafar
  • Longobardi, Maria

Abstract

A policy plan is proposed for the warranty length of a repairable system which consists of a number of minimal repairs and duration of service. For this purpose, two total expected costs of the manufacturer are considered as the objective of the optimization model and the conditions for finding unique optimal solutions are investigated. To illustrate the performance of the theoretical results, numerical computations and graphical analysis are given when the lifetime of the original system follows an exponential distribution or a Weibull distribution. Finally, a real data set corresponds to the time between failures of a compressor is used to demonstrate the results.

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  • Hooti, Fatemeh & Ahmadi, Jafar & Longobardi, Maria, 2020. "Optimal extended warranty length with limited number of repairs in the warranty period," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020306128
    DOI: 10.1016/j.ress.2020.107111
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    References listed on IDEAS

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    Cited by:

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    3. Lijun Shang & Xiguang Yu & Liying Wang & Yongjun Du, 2022. "Design of Random Warranty and Maintenance Policy: From a Perspective of the Life Cycle," Mathematics, MDPI, vol. 10(20), pages 1-22, October.
    4. Shang, Lijun & Liu, Baoliang & Qiu, Qingan & Yang, Li & Du, Yongjun, 2023. "Designing warranty and maintenance policies for products subject to random working cycles," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Lijun Shang & Yongjun Du & Cang Wu & Chengye Ma, 2022. "A Bivariate Optimal Random Replacement Model for the Warranted Product with Job Cycles," Mathematics, MDPI, vol. 10(13), pages 1-16, June.
    6. Qiao, Peirui & Ma, Yizhong & Luo, Ming & Shen, Jingyuan & Zhou, Hanting, 2024. "Reliability modeling and warranty optimization for products with self-healing under a dynamic shock environment," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    7. Lijun Shang & Guojun Shang & Yongjun Du & Qingan Qiu & Li Yang & Qinglai Dong, 2022. "Post-Warranty Replacement Models for the Product under a Hybrid Warranty," Mathematics, MDPI, vol. 10(10), pages 1-18, May.
    8. Yongjun Du & Lijun Shang & Qingan Qiu & Li Yang, 2022. "Optimum Post-Warranty Maintenance Policies for Products with Random Working Cycles," Mathematics, MDPI, vol. 10(10), pages 1-16, May.
    9. Liu, Peng & Wang, Guanjun, 2023. "Generalized non-renewing replacement warranty policy and an age-based post-warranty maintenance strategy," European Journal of Operational Research, Elsevier, vol. 311(2), pages 567-580.
    10. Huang, Yeu-Shiang & Fang, Chih-Chiang & Lu, Chang-Ming & (Bill) Tseng, Tzu-Liang, 2022. "Optimal Warranty Policy for Consumer Electronics with Dependent Competing Failure Processes," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    11. Zhu, Ying & Xia, Tangbin & Hong, Ge & Chen, Zhen & Pan, Ershun & Xi, Lifeng, 2022. "Collaborative maintenance service and component sales under coopetition patterns for OEMs challenged by booming used-component sales," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    12. Liu, Peng & Wang, Guanjun & Su, Peng, 2021. "Optimal maintenance strategies for warranty products with limited repair time and limited repair number," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    13. Ram Niwas & M. S. Kadyan, 2022. "A bi-objective inspection policy for a repairable engineering system with failure free warranty," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 881-891, April.
    14. Giulio Mattera & Gianfranco Piscopo & Maria Longobardi & Massimiliano Giacalone & Luigi Nele, 2024. "Improving the Interpretability of Data-Driven Models for Additive Manufacturing Processes Using Clusterwise Regression," Mathematics, MDPI, vol. 12(16), pages 1-18, August.

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