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Reliability evaluation in terms of flow data mining for multistate networks

Author

Listed:
  • Yi-Kuei Lin

    (National Chiao Tung University
    Asia University
    Wenzhou University)

  • Shin-Guang Chen

    (Tungnan University
    Kaohsiung Medical University)

Abstract

Network reliability is famous for its problem solving ability in several real-life applications. However, due to its NP-hard nature (Ball in IEEE Trans Reliab 35(3):230–238, 1986), researchers are devoted to the improvement of computational efficiency in various approaches. Although flow in networks depicts its combination properties, only few of them are useful in the calculation of network reliability. In some point of views, we call it mining in flow data. This paper presents techniques of how to efficiently do the flow data mining tasks. A skill based on backtrack and maximal flow is illustrated with examples and benchmarks. The results show that the proposed approach is valuable in the calculation of network reliability.

Suggested Citation

  • Yi-Kuei Lin & Shin-Guang Chen, 2022. "Reliability evaluation in terms of flow data mining for multistate networks," Annals of Operations Research, Springer, vol. 311(1), pages 225-237, April.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-020-03774-7
    DOI: 10.1007/s10479-020-03774-7
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    References listed on IDEAS

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    1. Xiu-Zhen Xu & Yi-Feng Niu & Qing Li, 2019. "Efficient Enumeration of - Minimal Paths in Reliability Evaluation of Multistate Networks," Complexity, Hindawi, vol. 2019, pages 1-10, March.
    2. Majid Forghani-elahabad & Nelson Kagan, 2019. "Reliability evaluation of a stochastic-flow network in terms of minimal paths with budget constraint," IISE Transactions, Taylor & Francis Journals, vol. 51(5), pages 547-558, May.
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