Exact reliability optimization for series-parallel graphs using convex envelopes

TL;DR

This paper introduces a novel convex envelope-based optimization framework for exact reliability maximization in series-parallel networks with independent, non-identical failure probabilities, enabling efficient solutions to complex network design problems.

Contribution

It develops a new convex envelope approach for nonconvex reliability optimization in series-parallel graphs, improving solution efficiency and accuracy.

Findings

Efficiently finds optimal solutions for challenging reliability instances.

Uses convex envelopes and spatial branch-and-bound for nonconvex optimization.

Demonstrates effectiveness on complex network design problems.

Abstract

Given its wide spectrum of applications, the classical problem of all-terminal network reliability evaluation remains a highly relevant problem in network design. The associated optimization problem -- to find a network with the best possible reliability under multiple constraints -- presents an even more complex challenge, which has been addressed in the scientific literature but usually under strong assumptions over failures probabilities and/or the network topology. In this work, we propose a novel reliability optimization framework for network design with failures probabilities that are independent but not necessarily identical. We leverage the linear-time evaluation procedure for network reliability in the series-parallel graphs of Satyanarayana and Wood(1985) to formulate the reliability optimization problem as a mixed-integer nonlinear optimization problem. To solve this nonconvex problem, we use classical convex envelopes of bilinear functions, introduce custom cutting planes, and propose a new family of convex envelopes for expressions that appear in the evaluation of network reliability. Furthermore, we exploit the refinements produced by spatial branch-and-bound to locally strengthen our convex relaxations. Our experiments show that, using our framework, one can efficiently obtain optimal solutions in challenging instances of this problem.