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Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

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  • Fazlollahtabar, Hamed
  • Saidi-Mehrabad, Mohammad
  • Balakrishnan, Jaydeep

Abstract

This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted.

Suggested Citation

  • Fazlollahtabar, Hamed & Saidi-Mehrabad, Mohammad & Balakrishnan, Jaydeep, 2015. "Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 34-44.
    • Handle: RePEc:eee:reensy:v:135:y:2015:i:c:p:34-44
    DOI: 10.1016/j.ress.2014.11.004
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    References listed on IDEAS

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    1. Lisnianski, Anatoly & Elmakias, David & Laredo, David & Ben Haim, Hanoch, 2012. "A multi-state Markov model for a short-term reliability analysis of a power generating unit," Reliability Engineering and System Safety, Elsevier, vol. 98(1), pages 1-6.
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    3. Hamed Fazlollahtabar & Mohammad Saidi-Mehrabad, 2013. "Optimising a multi-objective reliability assessment in multiple AGV manufacturing system," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 16(3), pages 352-372.
    4. Michael O. Ball & Feng L. Lin, 1993. "A Reliability Model Applied to Emergency Service Vehicle Location," Operations Research, INFORMS, vol. 41(1), pages 18-36, February.
    5. Gregory Levitin, 2005. "The Universal Generating Function in Reliability Analysis and Optimization," Springer Series in Reliability Engineering, Springer, number 978-1-84628-245-4, February.
    6. Takamura, Yoshiharu & Tone, Kaoru, 2003. "A comparative site evaluation study for relocating Japanese government agencies out of Tokyo," Socio-Economic Planning Sciences, Elsevier, vol. 37(2), pages 85-102, June.
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    Cited by:

    1. Karimi, Behzad & Niaki, S.T.A. & Haleh, Hassan & Naderi, Bahman, 2018. "Bi-objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 92-104.
    2. Yan, R. & Dunnett, S.J. & Jackson, L.M., 2022. "Model-Based Research for Aiding Decision-Making During the Design and Operation of Multi-Load Automated Guided Vehicle Systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Behzad Karimi & Seyed Taghi Akhavan Niaki & Amir Hossein Niknamfar & Mahsa Gareh Hassanlu, 2021. "Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm," Journal of Risk and Reliability, , vol. 235(2), pages 306-328, April.

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