Enhancing Binary-State Network Reliability with Layer-Cut BAT-MCS
This paper introduces layer-cut BAT-MCS, an enhanced algorithm for binary-state network reliability assessment. The original BAT-MCS integrates the deterministic Binary Addition Tree (BAT) algorithm with stochastic Monte Carlo simulation (MCS) in terms of the novel supervectors, creating a self-regu...
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| Published in: | Reliability engineering & system safety Vol. 264; p. 111446 |
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| Format: | Journal Article |
| Language: | English |
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01.12.2025
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| ISSN: | 0951-8320 |
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| Abstract | This paper introduces layer-cut BAT-MCS, an enhanced algorithm for binary-state network reliability assessment. The original BAT-MCS integrates the deterministic Binary Addition Tree (BAT) algorithm with stochastic Monte Carlo simulation (MCS) in terms of the novel supervectors, creating a self-regulating mechanism that reduces variance and improves efficiency. Despite its advantages, BAT-MCS exhibits limitations in supervector selection methodology and computational complexity of approximate reliability calculations. The proposed layer-cut BAT-MCS addresses these weaknesses through a novel layer-cut approach for supervector selection that significantly outperforms traditional min-cut methods. This innovation simplifies MCS complexity while maintaining comprehensive network analysis capabilities. Extensive numerical experiments conducted on small and medium-sized binary-state networks demonstrate that layer-cut BAT-MCS achieves superior computational efficiency and accuracy compared to both traditional MCS and the original BAT-MCS implementations. The results indicate that the layer-cut technique provides a more efficient network decomposition strategy, substantially reducing both runtime and variance. These improvements make layer-cut BAT-MCS particularly valuable for reliability assessment of small-scale or sparse network systems where computational resources are limited and high accuracy is required. |
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| AbstractList | This paper introduces layer-cut BAT-MCS, an enhanced algorithm for binary-state network reliability assessment. The original BAT-MCS integrates the deterministic Binary Addition Tree (BAT) algorithm with stochastic Monte Carlo simulation (MCS) in terms of the novel supervectors, creating a self-regulating mechanism that reduces variance and improves efficiency. Despite its advantages, BAT-MCS exhibits limitations in supervector selection methodology and computational complexity of approximate reliability calculations. The proposed layer-cut BAT-MCS addresses these weaknesses through a novel layer-cut approach for supervector selection that significantly outperforms traditional min-cut methods. This innovation simplifies MCS complexity while maintaining comprehensive network analysis capabilities. Extensive numerical experiments conducted on small and medium-sized binary-state networks demonstrate that layer-cut BAT-MCS achieves superior computational efficiency and accuracy compared to both traditional MCS and the original BAT-MCS implementations. The results indicate that the layer-cut technique provides a more efficient network decomposition strategy, substantially reducing both runtime and variance. These improvements make layer-cut BAT-MCS particularly valuable for reliability assessment of small-scale or sparse network systems where computational resources are limited and high accuracy is required. |
| ArticleNumber | 111446 |
| Author | Yeh, Wei-Chang |
| Author_xml | – sequence: 1 givenname: Wei-Chang orcidid: 0000-0001-7393-0768 surname: Yeh fullname: Yeh, Wei-Chang email: yeh@ieee.org organization: Department of Industrial Engineering and Engineering Management, National Tsing Hua University |
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| Cites_doi | 10.1017/S0269964800003387 10.1016/j.ress.2020.107048 10.1016/j.ress.2021.107822 10.1287/opre.34.4.581 10.1016/j.ress.2024.109990 10.1016/j.ress.2023.109378 10.1016/j.ress.2022.108796 10.1016/j.ress.2022.108371 10.1103/PhysRevLett.134.020601 10.1007/s10479-024-06377-8 10.1016/j.patrec.2024.03.013 10.1016/j.asoc.2023.110743 10.1109/TR.2018.2807198 10.1080/07408170802322622 10.1007/s10479-022-04911-0 10.1016/S0888-613X(97)10004-4 10.1016/j.ress.2021.107950 10.1109/TR.2023.3312752 10.1007/s10479-024-06409-3 10.1016/j.ress.2023.109297 10.1016/j.ress.2022.108557 10.1016/j.ress.2021.107448 10.1111/risa.13820 10.1016/j.ress.2022.108564 10.1504/IJOR.2020.105764 10.1016/j.ress.2021.107917 10.1016/j.eswa.2009.09.070 10.1016/j.ress.2022.108970 10.1016/j.ress.2024.110376 10.1016/j.ress.2023.109175 10.1016/j.ress.2023.109102 10.1016/j.ress.2005.08.002 |
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| Keywords | Supervector Binary-Addition-Tree algorithm (BAT) Binary-State Network Reliability, Monte Carlo Simulation (MCS) BAT-MCS |
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| SubjectTerms | BAT-MCS Binary-Addition-Tree algorithm (BAT) Binary-State Network Reliability, Monte Carlo Simulation (MCS) Supervector |
| Title | Enhancing Binary-State Network Reliability with Layer-Cut BAT-MCS |
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