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Simulation-based system reliability estimation of a multi-state flow network for all possible demand levels

Author

Listed:
  • Ping-Chen Chang

    (National Taipei University of Technology)

  • Ding-Hsiang Huang

    (Tunghai University)

  • Cheng-Fu Huang

    (Feng Chia University)

Abstract

The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as Rd, is defined as the probability of successfully transmitting at least d units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating Rd are basically designed for predetermined values of d. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified d. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm.

Suggested Citation

  • Ping-Chen Chang & Ding-Hsiang Huang & Cheng-Fu Huang, 2024. "Simulation-based system reliability estimation of a multi-state flow network for all possible demand levels," Annals of Operations Research, Springer, vol. 340(1), pages 117-132, September.
    • Handle: RePEc:spr:annopr:v:340:y:2024:i:1:d:10.1007_s10479-024-06141-y
    DOI: 10.1007/s10479-024-06141-y
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    References listed on IDEAS

    as
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    6. Ping-Chen Chang, 2019. "Reliability estimation for a stochastic production system with finite buffer storage by a simulation approach," Annals of Operations Research, Springer, vol. 277(1), pages 119-133, June.
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    8. Chen, Qian & Zuo, Lili & Wu, Changchun & Cao, Yankai & Bu, Yaran & Chen, Feng & Sadiq, Rehan, 2021. "Supply reliability assessment of a gas pipeline network under stochastic demands," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
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    Full references (including those not matched with items on IDEAS)

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