A simple and effective hybrid genetic search for the job sequencing and tool switching problem
This work addresses a practical operations research problem for manufacturing and scheduling, but it is incremental as it builds on existing methods with improvements.
The paper tackled the job sequencing and tool switching problem (SSP) by proposing a hybrid genetic search algorithm, which significantly outperformed all previous approaches on classical benchmark instances.
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.