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A quick BAT for evaluating the reliability of binary-state networks

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  • Yeh, Wei-Chang

Abstract

Network structures and models have been widely adopted, and many are based on the binary-state network. Reliability is the most commonly used tool to evaluate network performance. Efficient algorithms to evaluate binary-state network reliability are continually being developed. The motivation of this study is to propose an efficient algorithm called the quick BAT to evaluate binary-state network reliability. The propose quick BAT is based on the binary-addition tree algorithm (BAT) and employs three novel concepts: the first connected vector, the last disconnected vector, and super vectors. These super vectors narrow the search space and the calculations of their occurrent probabilities simplify the probability calculations to reduce the run time of the algorithm. Moreover, we show that replacing each undirected arc with two directed arcs, which is required in traditional direct methods, is unnecessary in the proposed algorithm. We call this novel concept the undirected vectors. The advantage and performance of the proposed quick BAT algorithm was verified experimentally by solving 20 benchmark problems and compared to the binary decision diagram (BDD), quick inclusion–exclusion technology (QIE), and BAT.

Suggested Citation

  • Yeh, Wei-Chang, 2021. "A quick BAT for evaluating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004336
    DOI: 10.1016/j.ress.2021.107917
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    References listed on IDEAS

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    Cited by:

    1. Yeh, Wei-Chang, 2024. "Time-reliability optimization for the stochastic traveling salesman problem," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Yeh, Wei-Chang & Tan, Shi-Yi & Forghani-elahabad, Majid & Khadiri, Mohamed El & Jiang, Yunzhi & Lin, Chen-Shiun, 2022. "New binary-addition tree algorithm for the all-multiterminal binary-state network reliability problem," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Yeh, Wei-Chang & Du, Chia-Ming & Tan, Shi-Yi & Forghani-elahabad, Majid, 2023. "Application of LSTM based on the BAT-MCS for binary-state network approximated time-dependent reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Wu, Baichao & Sun, Long, 2024. "A novel layer-by-layer recursive decomposition algorithm for calculation of network reliability," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Yeh, Wei-Chang, 2022. "Novel direct algorithm for computing simultaneous all-level reliability of multistate flow networks," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Yeh, Wei-Chang & Tan, Shi-Yi & Zhu, Wenbo & Huang, Chia-Ling & Yang, Guang-yi, 2022. "Novel binary addition tree algorithm (BAT) for calculating the direct lower-bound of the highly reliable binary-state network reliability," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. Chen, Liwei & Cheng, Chunchun & Dui, Hongyan & Xing, Liudong, 2022. "Maintenance cost-based importance analysis under different maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    8. Chan, Jianpeng & Papaioannou, Iason & Straub, Daniel, 2022. "An adaptive subset simulation algorithm for system reliability analysis with discontinuous limit states," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. 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).
    10. Yeh, Wei-Chang, 2022. "Novel self-adaptive Monte Carlo simulation based on binary-addition-tree algorithm for binary-state network reliability approximation," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Davila-Frias, Alex & Yodo, Nita & Le, Trung & Yadav, Om Prakash, 2023. "A deep neural network and Bayesian method based framework for all-terminal network reliability estimation considering degradation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    12. Qiuju Zhong & Ying Wang & Wu Lv & Jie Xu & Yichun Zhang, 2022. "Self-Regulation, Teaching Presence, and Social Presence: Predictors of Students’ Learning Engagement and Persistence in Blended Synchronous Learning," Sustainability, MDPI, vol. 14(9), pages 1-18, May.
    13. 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).
    14. Yeh, Wei-Chang & Zhu, Wenbo & Tan, Shi-Yi & Wang, Gai-Ge & Yeh, Yuan-Hui, 2022. "Novel general active reliability redundancy allocation problems and algorithm," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).

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