Exact and approximation algorithms for the expanding search problem

TL;DR

This paper introduces exact and heuristic algorithms for the expanding search problem, optimizing search sequences in weighted graphs to minimize expected search time, with applications in security, mining, and disaster relief.

Contribution

It develops a branch-and-cut algorithm, a greedy approximation, and a local search method, advancing solution techniques for the expanding search problem.

Findings

Branch-and-cut outperforms existing methods

Heuristics find near-optimal solutions quickly

Algorithms applicable to real-world search scenarios

Abstract

Suppose a target is hidden in one of the vertices of an edge-weighted graph according to a known probability distribution. The expanding search problem asks for a search sequence of the vertices so as to minimize the expected time for finding the target, where the time for reaching the next vertex is determined by its distance to the region that was already searched. This problem has numerous applications, such as searching for hidden explosives, mining coal, and disaster relief. In this paper, we develop exact algorithms and heuristics, including a branch-and-cut procedure, a greedy algorithm with a constant-factor approximation guarantee, and a novel local search procedure based on a spanning tree neighborhood. Computational experiments show that our branch-and-cut procedure outperforms all existing methods for general instances and both heuristics compute near-optimal solutions with little computational effort.