A Monte Carlo sampling plan for estimating network reliability
Operations Research
Network reliability evaluation
Network performance modeling and simulation
The Combinatorics of Network Reliability
The Combinatorics of Network Reliability
Basic Concepts and Taxonomy of Dependable and Secure Computing
IEEE Transactions on Dependable and Secure Computing
WSEAS TRANSACTIONS on COMMUNICATIONS
Rare Event Simulation using Monte Carlo Methods
Rare Event Simulation using Monte Carlo Methods
Paths of Bounded Length and Their Cuts: Parameterized Complexity and Algorithms
Parameterized and Exact Computation
Models of Network Reliability: Analysis, Combinatorics, and Monte Carlo
Models of Network Reliability: Analysis, Combinatorics, and Monte Carlo
Proceedings of the Winter Simulation Conference
Static Network Reliability Estimation via Generalized Splitting
INFORMS Journal on Computing
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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 a performability measure that depends on the hop distance between terminal nodes. The new measure generalises the diameter-constrained network reliability measure. We propose a Monte Carlo method with significant variance reduction compared to crude Monte Carlo. It is based on using edge sets named d-pathsets and d-cutsets for reducing the variance of the estimator. These edge sets, considered as a priori known in previous literature, heavily 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.