A simulation Method for Network Performability Estimation using Heuristically-computed Pathsets and Cutsets

TL;DR

This paper presents a simulation method for network performability estimation that employs heuristically-selected pathsets and cutsets to improve variance reduction in Monte Carlo simulations, enhancing efficiency in reliability analysis.

Contribution

It introduces and compares heuristics for selecting edge sets, improving Monte Carlo-based performability estimation over previous methods.

Findings

Significant efficiency improvements with heuristic-based edge set selection.

Effective variance reduction compared to crude Monte Carlo.

Numerical examples demonstrate practical benefits of the approach.

Abstract

Consider a set of terminal nodes K that belong to a network whose nodes are connected by links that fail independently with known probabilities. We introduce a method for estimating any performability measure that depends on the hop distance between terminal nodes. It generalises previously introduced Monte Carlo methods for estimation of the K-reliability of networks with variance reduction compared to crude Monte Carlo. They are based on using sets of edges named d-pathsets and d-cutsets for reducing the variance of the estimator. These sets of edges, considered as a priori known in previous literature, heaviliy affect the attained performance; we hereby introduce and compare a family of heuristics for their selection. Numerical examples are presented, showing the significant efficiency improvements that can be obtained by chaining the edge set selection heuristics to the proposed Monte Carlo sampling plan.