A simple and effective hybrid genetic search for the job sequencing and tool switching problem

TL;DR

This paper introduces a simple yet effective hybrid genetic algorithm for the job sequencing and tool switching problem, significantly improving solution quality on benchmark instances through tailored operators and diversity strategies.

Contribution

The paper presents a novel hybrid genetic search with specialized operators and a secondary objective, outperforming previous methods on classical and larger benchmark instances.

Findings

Outperforms all previous approaches on benchmark instances

Effectively explores solution space with diversity management

Successfully scales to larger problem instances

Abstract

The job sequencing and tool switching problem (SSP) has been extensively studied in the field of operations research, due to its practical relevance and methodological interest. Given a machine that can load a limited amount of tools simultaneously and a number of jobs that require a subset of the available tools, the SSP seeks a job sequence that minimizes the number of tool switches in the machine. To solve this problem, we propose a simple and efficient hybrid genetic search based on a generic solution representation, a tailored decoding operator, efficient local searches and diversity management techniques. To guide the search, we introduce a secondary objective designed to break ties. These techniques allow to explore structurally different solutions and escape local optima. As shown in our computational experiments on classical benchmark instances, our algorithm significantly outperforms all previous approaches while remaining simple to apprehend and easy to implement. We finally report results on a new set of larger instances to stimulate future research and comparative analyses.