Powering stochastic reliability models by discrete event simulation

Igor Kozine, Xiaoyun Wang

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    Abstract

    Markov reliability models are widely practiced tools for the analysis of repairable systems. Nevertheless, the assumptions of the Markov model may appear too restrictive to adequately model a real system and the explosion in the number of states as the size of the system increases may make it difficult to find a solution to the problem. The power of modern computers and recent developments in discrete-event simulation (DES) software enable to diminish some of the drawbacks of stochastic models. In this paper we describe the insights we have gained based on using both Markov and DES models for simple systems. By contrasting the results of the two models we illuminate their advantages and disadvantages as well as we conclude that it is a good way of model validation.
    Original languageEnglish
    Title of host publicationInternational Conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation
    PublisherUniversity of Bordeaux
    Publication date2012
    Pages130-135
    Publication statusPublished - 2012
    EventInternational Conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation - University of Bordeaux, Bordeaux, France
    Duration: 4 Jul 20126 Jul 2012

    Conference

    ConferenceInternational Conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation
    LocationUniversity of Bordeaux
    CountryFrance
    CityBordeaux
    Period04/07/201206/07/2012

    Keywords

    • Markov reliability model
    • Discrete event simulation

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