  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.
