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An improved bounding algorithm for approximating multistate network reliability based on state-space decomposition method

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  • Liu, Tao
  • Bai, Guanghan
  • Tao, Junyong
  • Zhang, Yun-An
  • Fang, Yining

Abstract

The evaluation of two-terminal multistate network reliability precisely is a NP-hard problem. Approximating the reliability bounds with state space decomposition is an effective method to tradeoff the computational effort and the acceptable reliability approximate value when the scale of the network is relatively large. This decomposition process cannot avoid the shortage of a large amount of computational effort consumed by sets of unspecified states with little contributions to reliability bounds. Thus, preset critical values are used to filter out sets of unspecified states with less probability. However, managers cannot predict the preset critical value that satisfies their demand. Therefore, we first proposed a serial bounding algorithm based on the breadth-first mechanism, wherein sets of unspecified states on the same generation are all decomposed before moving to the next generation. The mechanism of combining serial computing with parallel computing is further developed to fully utilize computer capability. In addition, the preset critical values are not required before running the algorithm. The results of efficiency comparison demonstrate that the proposed algorithm can significantly improve the efficiency for approximating multistate network reliability. Stability investigations show that the computational efficiency of the proposed algorithm is more stable and effective under various component state distributions.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000648
    DOI: 10.1016/j.ress.2021.107500
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    Citations

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    Cited by:

    1. Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-An & Fang, Yining, 2024. "A Multistate Network Approach for Resilience Analysis of UAV Swarm considering Information Exchange Capacity," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Zhou, Yifan & Liu, Libo & Li, Hao, 2022. "Reliability estimation and optimisation of multistate flow networks using a conditional Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 221(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. Kozyra, Paweł Marcin, 2023. "The usefulness of (d,b)-MCs and (d,b)-MPs in network reliability evaluation under delivery or maintenance cost constraints," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Li, Shunlong & Wang, Jie & He, Shaoyang, 2023. "Connectivity probability evaluation of a large-scale highway bridge network using network decomposition," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    6. Niu, Yi-Feng & Song, Yi-Fan & Xu, Xiu-Zhen & Zhao, Xia, 2022. "Efficient reliability computation of a multi-state flow network with cost constraint," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Lin, Shuai & Jia, Limin & Zhang, Hengrun & Zhang, Pengzhu, 2022. "Reliability of high-speed electric multiple units in terms of the expanded multi-state flow network," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Niu, Yi-Feng & Zhao, Xia & Xu, Xiu-Zhen & Zhang, Shi-Yun, 2023. "Reliability assessment of a stochastic-flow distribution network with carbon emission constraint," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Xu, Bei & Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-an & Fang, Yining, 2022. "A multistate network approach for reliability evaluation of unmanned swarms by considering information exchange capacity," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    10. 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).
    11. Forghani-elahabad, Majid & Yeh, Wei-Chang, 2022. "An improved algorithm for reliability evaluation of flow networks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    12. 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).
    13. Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-An & Fang, Yining & Xu, Bei, 2022. "Modeling and evaluation method for resilience analysis of multi-state networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    14. Xu, Xiu-Zhen & Niu, Yi-Feng & Song, Yi-Fan, 2021. "Computing the reliability of a stochastic distribution network subject to budget constraint," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Cui, Hongjun & Wang, Fei & Ma, Xinwei & Zhu, Minqing, 2022. "A novel fixed-node unconnected subgraph method for calculating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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