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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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 264; p. 111446
Main Author: Yeh, Wei-Chang
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2025
Subjects:
BAT-MCS
Binary-Addition-Tree algorithm (BAT)
Binary-State Network Reliability, Monte Carlo Simulation (MCS)
Supervector
Supervector
Binary-Addition-Tree algorithm (BAT)
Binary-State Network Reliability, Monte Carlo Simulation (MCS)
BAT-MCS
ISSN:0951-8320
Online Access:Get full text
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Summary: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.
ISSN:0951-8320
DOI:10.1016/j.ress.2025.111446