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Selective Maintenance Optimization Under Uncertainties

In: Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis

Author

Listed:
  • Yu Liu

    (University of Electronic Science and Technology of China)

  • Tangfan Xiahou

    (University of Electronic Science and Technology of China)

  • Tao Jiang

    (University of Electronic Science and Technology of China)

Abstract

Due to limited maintenance resources, such as budget, time, and manpower, selective maintenance has widespread applications in both industry and military environments. By a selective maintenance strategy, a subset of feasible maintenance actions for a repairable system can be selected to be performed so as to ensure the success of the subsequent mission. However, in engineering practices, various uncertainties are inevitable in selective maintenance. This chapter offers a comprehensive review on the existing selective maintenance models under uncertainties. Additionally, as complements to the existing models, two new selective maintenance models by taking account of the uncertainty associated with the durations of breaks and maintenance actions and the uncertainty associated with imperfect observations are put forth. Two illustrative examples are presented to demonstrate the effectiveness of the proposed models.

Suggested Citation

  • Yu Liu & Tangfan Xiahou & Tao Jiang, 2022. "Selective Maintenance Optimization Under Uncertainties," International Series in Operations Research & Management Science, in: Adiel Teixeira de Almeida & Love Ekenberg & Philip Scarf & Enrico Zio & Ming J. Zuo (ed.), Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis, pages 487-508, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-89647-8_23
    DOI: 10.1007/978-3-030-89647-8_23
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    Cited by:

    1. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2024. "Stochastic programming for selective maintenance optimization with uncertainty in the next mission conditions," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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