A dynamic unreliability assessment and optimal maintenance strategies for multistate weighted k-out-of-n: F systems

Hadi Akbarzade Khorshidi, Indra Gunawan, Yousef Ibrahim

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    In this paper, a dynamic evaluation of the multistate weighted k-out-of-n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure cost provides an opportunity to employ financial concepts in system unreliability estimation. Hence, system unreliability and system cost can be compared easily in order to making decision. The components' probabilities are computed over time to model the dynamic behavior of the system. The whole system has been assessed by recursive algorithm approach. As a result, a bi-objective optimization model can be developed to find optimal decisions on maintenance strategies. Finally, the application of the proposed model is investigated via a transportation system case. Matlab programming is developed for the case, and genetic algorithm is used to solve the optimization model.

    Original languageEnglish
    Pages (from-to)485-493
    Number of pages9
    JournalApplied Stochastic Models in Business and Industry
    Volume32
    DOIs
    Publication statusPublished - 2016

    Keywords

    • Bi-objective optimization
    • Dynamic assessment
    • Failure cost
    • Multistate weighted k-out-of-n:F system
    • Recursive algorithm
    • Unreliability evaluation

    Cite this

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    title = "A dynamic unreliability assessment and optimal maintenance strategies for multistate weighted k-out-of-n: F systems",
    abstract = "In this paper, a dynamic evaluation of the multistate weighted k-out-of-n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure cost provides an opportunity to employ financial concepts in system unreliability estimation. Hence, system unreliability and system cost can be compared easily in order to making decision. The components' probabilities are computed over time to model the dynamic behavior of the system. The whole system has been assessed by recursive algorithm approach. As a result, a bi-objective optimization model can be developed to find optimal decisions on maintenance strategies. Finally, the application of the proposed model is investigated via a transportation system case. Matlab programming is developed for the case, and genetic algorithm is used to solve the optimization model.",
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    A dynamic unreliability assessment and optimal maintenance strategies for multistate weighted k-out-of-n : F systems. / Khorshidi, Hadi Akbarzade; Gunawan, Indra; Ibrahim, Yousef.

    In: Applied Stochastic Models in Business and Industry, Vol. 32, 2016, p. 485-493.

    Research output: Contribution to journalArticleResearchpeer-review

    TY - JOUR

    T1 - A dynamic unreliability assessment and optimal maintenance strategies for multistate weighted k-out-of-n

    T2 - F systems

    AU - Khorshidi, Hadi Akbarzade

    AU - Gunawan, Indra

    AU - Ibrahim, Yousef

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    AB - In this paper, a dynamic evaluation of the multistate weighted k-out-of-n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure cost provides an opportunity to employ financial concepts in system unreliability estimation. Hence, system unreliability and system cost can be compared easily in order to making decision. The components' probabilities are computed over time to model the dynamic behavior of the system. The whole system has been assessed by recursive algorithm approach. As a result, a bi-objective optimization model can be developed to find optimal decisions on maintenance strategies. Finally, the application of the proposed model is investigated via a transportation system case. Matlab programming is developed for the case, and genetic algorithm is used to solve the optimization model.

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    KW - Failure cost

    KW - Multistate weighted k-out-of-n:F system

    KW - Recursive algorithm

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