A constraint satisfaction approach to the robust spanning tree problem with interval data

TL;DR

This paper introduces a constraint satisfaction method for solving the robust spanning tree problem with interval data, significantly improving solution efficiency over previous mathematical programming approaches in telecommunication applications.

Contribution

It presents a novel constraint satisfaction approach incorporating a combinatorial lower bound, pruning, and a strategic search to effectively solve robust spanning tree problems with interval data.

Findings

Algorithm outperforms previous mathematical programming methods

Enlarges the class of solvable problems

Achieves significant computational improvements

Abstract

Robust optimization is one of the fundamental approaches to deal with uncertainty in combinatorial optimization. This paper considers the robust spanning tree problem with interval data, which arises in a variety of telecommunication applications. It proposes a constraint satisfaction approach using a combinatorial lower bound, a pruning component that removes infeasible and suboptimal edges, as well as a search strategy exploring the most uncertain edges first. The resulting algorithm is shown to produce very dramatic improvements over the mathematical programming approach of Yaman et al. and to enlarge considerably the class of problems amenable to effective solutions