A matheuristic for solving the single row facility layout problem

TL;DR

This paper introduces a novel matheuristic combining exact optimization and simulated annealing to effectively solve large-scale single row facility layout problems, outperforming existing methods on benchmark instances.

Contribution

The paper presents the first matheuristic specifically designed for SRFLP, integrating mixed-integer programming within a simulated annealing framework for improved solutions.

Findings

Improves best-known solutions for 17 out of 70 instances.

Matches best-known solutions for remaining instances.

Outperforms current state-of-the-art metaheuristics.

Abstract

The single row facility layout problem (SRFLP) is a well-studied NP-hard combinatorial optimization problem with applications in manufacturing and logistics systems. In the SRFLP, a set of facilities with lengths is given, as well as weights between each pair of facilities. The facilities must be arranged on a line, such that the sum of the weighted center-to-center distances is minimized. In this work, we introduce a novel matheuristic approach that integrates exact optimization into a metaheuristic framework based on simulated annealing to effectively solve large-scale SRFLP instances. Specifically, we propose the window approach matheuristic, which solves subsegments of the layout to optimality using mixed-integer programming while preserving the ordering of facilities outside the window. To the best of our knowledge, this constitutes the first matheuristic approach specifically designed for the SRFLP. We evaluate the performance of our method on the widely-used benchmark instance sets from literature. The computational results demonstrate that our matheuristic improves the best-known solution values for 17 of 70 instances, and matches the best-known solution values for the remaining 53 instances, outperforming current state-of-the-art metaheuristics.