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Mean time to failure and availability of semi-Markov missions with maximal repair

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  • Çekyay, B.
  • Özekici, S.

Abstract

We analyze mean time to failure and availability of semi-Markov missions that consist of phases with random sequence and durations. It is assumed that the system is a complex one with nonidentical components whose failure properties depend on the mission process. The stochastic structure of the mission is described by a Markov renewal process. We characterize mean time to failure and system availability under the maximal repair policy where the whole system is replaced by a brand new after successfully completing a phase before the next phase starts. Special cases involving Markovian missions are also considered to obtain explicit formulas.

Suggested Citation

  • Çekyay, B. & Özekici, S., 2010. "Mean time to failure and availability of semi-Markov missions with maximal repair," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1442-1454, December.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:3:p:1442-1454
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    References listed on IDEAS

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    1. Zhang, Yuan Lin & Wang, Guan Jun, 2007. "A deteriorating cold standby repairable system with priority in use," European Journal of Operational Research, Elsevier, vol. 183(1), pages 278-295, November.
    2. Der Kiureghian, Armen & Ditlevsen, Ove D. & Song, Junho, 2007. "Availability, reliability and downtime of systems with repairable components," Reliability Engineering and System Safety, Elsevier, vol. 92(2), pages 231-242.
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    Cited by:

    1. Huang, Yonghui & Guo, Xianping, 2011. "Finite horizon semi-Markov decision processes with application to maintenance systems," European Journal of Operational Research, Elsevier, vol. 212(1), pages 131-140, July.
    2. Bora Çekyay, 2021. "Reliability of mission-based k-out-of-n systems with exponential phase durations and component lifetimes," Journal of Risk and Reliability, , vol. 235(3), pages 446-457, June.
    3. Çekyay, Bora & Özekici, Süleyman, 2012. "Optimal maintenance of systems with Markovian mission and deterioration," European Journal of Operational Research, Elsevier, vol. 219(1), pages 123-133.
    4. Zhong, Chongquan & Jin, Haibo, 2014. "A novel optimal preventive maintenance policy for a cold standby system based on semi-Markov theory," European Journal of Operational Research, Elsevier, vol. 232(2), pages 405-411.
    5. Ruiz-Castro, Juan Eloy & Fernández-Villodre, Gemma, 2012. "A complex discrete warm standby system with loss of units," European Journal of Operational Research, Elsevier, vol. 218(2), pages 456-469.
    6. Jørgen Vitting Andersen & Roy Cerqueti & Giulia Rotundo, 2017. "Rational expectations and stochastic systems," Documents de travail du Centre d'Economie de la Sorbonne 17060, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Oct 2019.
    7. Yonghui Huang & Xianping Guo & Xinyuan Song, 2011. "Performance Analysis for Controlled Semi-Markov Systems with Application to Maintenance," Journal of Optimization Theory and Applications, Springer, vol. 150(2), pages 395-415, August.
    8. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2019. "Reliability-based measures and prognostic analysis of a K-out-of-N system in a random environment," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1120-1131.
    9. Li, Yan & Cui, Lirong & Lin, Cong, 2017. "Modeling and analysis for multi-state systems with discrete-time Markov regime-switching," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 41-49.
    10. Landon, Joshua & Özekici, Süleyman & Soyer, Refik, 2013. "A Markov modulated Poisson model for software reliability," European Journal of Operational Research, Elsevier, vol. 229(2), pages 404-410.

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