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Joint optimization of maintenance policy and inspection interval for a multi-unit series system using proportional hazards model

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  • Leila Jafari
  • Farnoosh Naderkhani
  • Viliam Makis

Abstract

Unlike the previous maintenance models of multi-unit systems which considered condition-based maintenance (CBM) or age information separately, we propose a novel optimization model which is characterized by a combination of CBM and age information using proportional hazards model. The preventive maintenance is applied for the main two units, where one unit is the core part of the system and subject to CM, and only the age information for the second main unit is available. Also, the other units are adjusted or replaced each time when the system is maintained. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process framework. The formula for the mean residual life of the system is derived, which is an important statistic in practical applications. A practical example of a multi-unit system from a mining company is provided, and a comparison with other policies shows an outstanding performance of the new model and the control policy proposed in this paper.

Suggested Citation

  • Leila Jafari & Farnoosh Naderkhani & Viliam Makis, 2018. "Joint optimization of maintenance policy and inspection interval for a multi-unit series system using proportional hazards model," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(1), pages 36-48, January.
    • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:1:p:36-48
    DOI: 10.1057/s41274-016-0160-9
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    Citations

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    Cited by:

    1. Zheng, Rui & Najafi, Seyedvahid & Zhang, Yingzhi, 2022. "A recursive method for the health assessment of systems using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Junyuan Wang & Xufeng Zhao & Jiawei Xiang, 2024. "Optimum design and replacement policies for k-out-of-n systems with deviation time and cost," Annals of Operations Research, Springer, vol. 340(1), pages 593-617, September.
    3. Wang, Jingjing & Miao, Yonghao, 2021. "Optimal preventive maintenance policy of the balanced system under the semi-Markov model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    4. Azar, Kamyar & Hajiakhondi-Meybodi, Zohreh & Naderkhani, Farnoosh, 2022. "Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Nooshin Salari & Viliam Makis, 2020. "Joint maintenance and just-in-time spare parts provisioning policy for a multi-unit production system," Annals of Operations Research, Springer, vol. 287(1), pages 351-377, April.
    6. Zheng, Rui & Chen, Bingkun & Gu, Liudong, 2020. "Condition-based maintenance with dynamic thresholds for a system using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    7. Najafi, Seyedvahid & Zheng, Rui & Lee, Chi-Guhn, 2021. "An optimal opportunistic maintenance policy for a two-unit series system with general repair using proportional hazards models," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Najafi, Seyedvahid & Lee, Chi-Guhn, 2023. "A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    9. Xu, Gaowei & Azhari, Fae, 2022. "Data-driven optimization of repair schemes and inspection intervals for highway bridges," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    10. Zheng, Rui & Wang, Jingjing & Zhang, Yingzhi, 2023. "A hybrid repair-replacement policy in the proportional hazards model," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1011-1021.

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