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Preventive maintenance optimization for a stochastically degrading system with a random initial age

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  • Sidibe, I.B.
  • Khatab, A.
  • Diallo, C.
  • Kassambara, A.

Abstract

This paper investigates the optimal age replacement policy for used systems, such as second-hand products, which start their second life-cycle in a more severe environment with an initial age that is uncertain. This uncertain age is modelled as a random variable following continuous probability distributions. A mathematical model is developed to minimize the total expected cost per unit of time for these systems on an infinite time horizon. Optimality and existence conditions for a unique optimal solution are derived and used in a numerical procedure to solve the problem. Numerical experiments are provided to demonstrate the added value and the impacts of the random initial age on the optimal replacement policy.

Suggested Citation

  • Sidibe, I.B. & Khatab, A. & Diallo, C. & Kassambara, A., 2017. "Preventive maintenance optimization for a stochastically degrading system with a random initial age," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 255-263.
  • Handle: RePEc:eee:reensy:v:159:y:2017:i:c:p:255-263
    DOI: 10.1016/j.ress.2016.11.018
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    References listed on IDEAS

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    Cited by:

    1. Santos, Augusto César de Jesus & Cavalcante, Cristiano Alexandre Virgínio & Wu, Shaomin, 2023. "Maintenance policies and models: A bibliometric and literature review of strategies for reuse and remanufacturing," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Santos, Augusto César de Jesus & Cavalcante, Cristiano Alexandre Virginio & Ren, Junru & Wu, Shaomin, 2023. "A novel delay time modelling method for incorporating reuse actions in three-state single-component systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.

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