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A path-based simulation approach for multistate flow network reliability estimation without using boundary points

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  • Chang, Ping-Chen

Abstract

A multistate flow network is useful for the effective and efficient model construction and reliability evaluation of large and complex systems. To evaluate the reliability of a multistate flow network, boundary points for specified demands are generated in the existing path- and cut-based methodologies. Using the boundary points, the system reliability can be calculated using analytical or simulation methodologies. However, evaluating reliability using boundary points is an NP-hard problem. To improve the time efficiency of reliability estimation, a path-based simulation approach without boundary points is proposed in this study, where the minimal path without any cycles is used. The contributions of this study are threefold: First, the proposed path-based simulation complements the cut-based simulation to improve the applicability of reliability estimation for various network topologies. Second, the proposed simulation algorithm presents linear time complexity, whereas conventional boundary-based analytical methodologies or simulations consume exponential/factorial time. Third, the time attribute is included in the proposed simulation approach to analyze the behavior of reliability degradation over time. The experimental results, including those of a case study, indicate that the proposed path-based simulation is more effective and efficient than existing boundary-based approaches, particularly for large and complex systems.

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  • Chang, Ping-Chen, 2024. "A path-based simulation approach for multistate flow network reliability estimation without using boundary points," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:reensy:v:249:y:2024:i:c:s0951832024003107
    DOI: 10.1016/j.ress.2024.110237
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    References listed on IDEAS

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    1. Thi-Phuong Nguyen, 2022. "Evaluation of network reliability for stochastic-flow air transportation network considering discounted fares from airlines," Annals of Operations Research, Springer, vol. 311(1), pages 335-355, April.
    2. Huang, Cheng-Fu & Huang, Ding-Hsiang & Lin, Yi-Kuei, 2022. "Network reliability evaluation for multi-state computing networks considering demand as the non-integer type," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Chang, Ping-Chen, 2022. "MC-based simulation approach for two-terminal multi-state network reliability evaluation without knowing d-MCs," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-An & Fang, Yining, 2021. "An improved bounding algorithm for approximating multistate network reliability based on state-space decomposition method," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    5. Cheng-Fu Huang, 2019. "Evaluation of system reliability for a stochastic delivery-flow distribution network with inventory," Annals of Operations Research, Springer, vol. 277(1), pages 33-45, June.
    6. Shi, Yan & Behrensdorf, Jasper & Zhou, Jiayan & Hu, Yue & Broggi, Matteo & Beer, Michael, 2024. "Network reliability analysis through survival signature and machine learning techniques," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Chang, Ping-Chen & Huang, Ding-Hsiang & Lin, Yi-Kuei & Nguyen, Thi-Phuong, 2021. "Reliability and maintenance models for a time-related multi-state flow network via d-MC approach," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Forghani-elahabad, Majid & Francesquini, Emilio, 2023. "Usage of task and data parallelism for finding the lower boundary vectors in a stochastic-flow network," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    9. Tiam Kapen, Pascalin & Jeutho Gouajio, Marinette & Yemélé, David, 2020. "Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: Application to the city of Bafoussam, Cameroon," Renewable Energy, Elsevier, vol. 159(C), pages 1188-1198.
    10. Paweł Marcin Kozyra, 2023. "An efficient algorithm for the reliability evaluation of multistate flow networks under budget constraints," IISE Transactions, Taylor & Francis Journals, vol. 55(11), pages 1091-1102, November.
    11. Huang, Cheng-Hao & Huang, Ding-Hsiang & Lin, Yi-Kuei, 2023. "Network reliability prediction for random capacitated-flow networks via an artificial neural network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. Xiaomei Liu & Naiming Xie, 2023. "Grey-based approach for estimating Weibull model and its application," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(21), pages 7601-7617, November.
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    Cited by:

    1. Huang, Ding-Hsiang, 2024. "An algorithm to generate all d-lower boundary points for a stochastic flow network using dynamic flow constraints," Reliability Engineering and System Safety, Elsevier, vol. 249(C).

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