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| Titre: | A New Cut-Based Genetic Algorithm for Graph Partitioning Applied to Cell Formation |
| Auteur(s): | Boulif, Menouar |
| Date de publication: | 2020 |
| Editeur: | springer |
| Référence bibliographique: | Heuristics for Optimization and Learning |
| Collection/Numéro: | Studies in Computational Intelligence;(SCI, volume 906) pp 269-284 |
| Résumé: | Cell formation is a critical step in the design of cellular manufacturing systems. Recently, it was tackled by using a cut-based-graph-partitioning model. This model meets real-life production systems requirements as it uses the actual amount of product flows, it looks for the suitable number of cells, and it takes into account the natural constraints such as operation sequences, maximum cell size, cohabitation and non-cohabitation constraints. Based on this model, we propose an original encoding representation to solve the problem by using a genetic algorithm. We discuss the performance of this new GA in comparison to some approaches taken from the literature when they are applied to a set of medium sized instances. Given the results we obtained, it is reasonable to assume that the new GA will provide similar results for large real-life instances. |
| URI/URL: | https://doi.org/10.1007/978-3-030-58930-1_18
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6051 |
| ISBN: | 978-3-030-58929-5
978-3-030-58930-1
https://link.springer.com/chapter/10.1007/978-3-030-58930-1_18 |
| Collection(s) : | Publications Internationales
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