Branch-and-bound algorithm for efficient reliability analysis of general coherent systems

TL;DR

This paper introduces the BRC algorithm, a novel branch-and-bound method that efficiently analyzes the reliability of general coherent systems by dynamically inferring component importance, demonstrated on a highway network benchmark.

Contribution

The paper presents a new BRC algorithm that extends branch-and-bound reliability analysis to general coherent systems, improving efficiency and applicability.

Findings

BRC efficiently finds minimal failure/survival event representations.

It dynamically infers component importance to reduce redundant analysis.

Applied to a highway network, it demonstrates real-time risk management potential.

Abstract

Branch and bound algorithms have been developed for reliability analysis of coherent systems. They exhibit a set of advantages; in particular, they can find a computationally efficient representation of a system failure or survival event, which can be re-used when the input probability distributions change over time or when new data is available. However, existing branch-and-bound algorithms can handle only a limited set of system performance functions, mostly network connectivity and maximum flow. Furthermore, they run redundant analyses on component vector states whose system state can be inferred from previous analysis results. This study addresses these limitations by proposing branch and bound for reliability analysis of general coherent systems} (BRC) algorithm: an algorithm that automatically finds minimal representations of failure/survival events of general coherent systems. Computational efficiency is attained by dynamically inferring importance of component events from hitherto obtained results. We demonstrate advantages of the BRC method as a real-time risk management tool by application to the Eastern Massachusetts highway benchmark network.