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Selective Maintenance under Stochastic Time Durations of Breaks and Maintenance Actions

In: Selective Maintenance Modelling and Optimization

Author

Listed:
  • Yu Liu

    (University of Electronic Science and Technology of China)

  • Hong-Zhong Huang

    (University of Electronic Science and Technology of China)

  • Tao Jiang

    (University of Electronic Science and Technology of China)

Abstract

Most traditional selective maintenance studies assume that the time durations of breaks and maintenance actions are pre-specified. Such an assumption for simplicity purpose may, however, not always hold in many industrial and military applications, and the maintenance time duration and the start time of the next mission may not be exactly known in advance. This chapter introduced a selective maintenance model under the stochastic time durations of breaks and maintenance actions. Due to these time duration uncertainties, the maintenance sequence in a break indeed affect the completion of the selected maintenance actions. The probability of a system successfully completing the next mission was evaluated based on the distribution of the number of completed maintenance actions, and the saddlepoint approximation was implemented to facilitate the computation of the involved multi-dimensional convolution. A tailored ant colony optimization algorithm was developed to solve the resulting combinational optimization problem in the cases of large-scale systems. A four-component system and a multi-state coal transportation system, which were respectively solved by an enumeration method and the tailored ant colony optimization algorithm, were exemplified in this chapter.

Suggested Citation

  • Yu Liu & Hong-Zhong Huang & Tao Jiang, 2023. "Selective Maintenance under Stochastic Time Durations of Breaks and Maintenance Actions," Springer Series in Reliability Engineering, in: Selective Maintenance Modelling and Optimization, chapter 0, pages 77-99, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-17323-3_5
    DOI: 10.1007/978-3-031-17323-3_5
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