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Firstly, I would like to thank my incredible advisor, Thibaut Vidal, for all the discussions, help and support in the past years and my coadvisor, Anand Subramanian, for helping making it fun to write a paper and becoming a great friend of mine in the process. I would also like to thank the examination committee, Marco Molinaro and Marcus Poggi, for their insightful comments and suggestions.I would like to thank my family, Elizabeth Zerpini, Ian Mecler, Katia Mecler, Rosinha Goldenstein, Nair Zerpini, Edir Semblano and specially my brother, Davi Mecler, who is also a great colleague in the area.My sincere gratitude also goes to my fellow colleagues, specially Rafael

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.