Accelerated Benders Decomposition and Local Branching for Dynamic Maximum Covering Location Problems

TL;DR

This paper introduces accelerated Benders decomposition and a novel local branching scheme to efficiently solve large-scale dynamic maximum covering location problems, advancing exact solution methods for complex, multi-period facility location challenges.

Contribution

It extends the branch-and-Benders-cut method with acceleration techniques and develops a specialized local branching approach with a new distance metric for dynamic MCLP.

Findings

Proposed methods outperform existing approaches on large-scale instances.

Acceleration techniques significantly reduce computation time.

New local branching scheme improves solution quality and efficiency.

Abstract

The maximum covering location problem (MCLP) is a key problem in facility location, with many applications and variants. One such variant is the dynamic (or multi-period) MCLP, which considers the installation of facilities across multiple time periods. To the best of our knowledge, no exact solution method has been proposed to tackle large-scale instances of this problem. To that end, in this work, we expand upon the current state-of-the-art branch-and-Benders-cut solution method in the static case, by exploring several acceleration techniques. Additionally, we propose a specialised local branching scheme, that uses a novel distance metric in its definition of subproblems and features a new method for efficient and exact solving of the subproblems. These methods are then compared through extensive computational experiments, highlighting the strengths of the proposed methodologies.