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Séminaire du GERAD

Learning variable neighborhood search for a job-scheduling problem


21 juin 2019   10h30 — 11h30

Nicolas Zufferey Professeur titulaire, GSEM, Université de Genève, Suisse

Variable neighborhood search is a local search metaheuristic that uses sequentially different neighborhood structures. This method has been successfully applied to various types of problems. In this work, variable neighborhood search is enhanced with a learning mechanism which helps to drive the search toward promising areas of the search space. The resulting method is applied to a single-machine scheduling problem with rejections, setups, and earliness and tardiness penalties. Experiments are conducted for instances from the literature. They show on the one hand the benefit of the learning mechanism (in terms of solution quality and robustness). On the other hand, the proposed method significantly outperforms state-of-the-art algorithms for the considered problem. Moreover, its flexibility allows its straightforward adaptation to other combinatorial optimization problems.

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Salle 4488
Pavillon André-Aisenstadt
Campus de l'Université de Montréal
2920, chemin de la Tour
Montréal QC H3T 1J4

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