Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm
Jeunet, Jully; Dellaert, Nico (2000), Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm, International Journal of Production Research, 38, 5, p. 1083-1099
TypeArticle accepté pour publication ou publié
Nom de la revueInternational Journal of Production Research
Taylor & Francis
MétadonnéesAfficher la notice complète
Résumé (EN)We develop a genetic algorithm (GA) to solve the uncapacitated multilevel lotsizing problem in material requirements planning (MRP) systems. The major drawback of existing approaches is undoubtedly their inability to provide costefficient solutions in a reasonable computation time for realistic size problems involving general product structures. By contrast, the proposed GA can easily handle large product structures (more than 500 items) with numerous common parts, a problem type for which standard optimization software memory becomes rapidly insufficient. Based upon several hybrid operators and an original way to build up the initial population, the resultant GA provides in a moderate execution time high cost-effectiveness solutions compared with other techniques, in the extensive tests we performed.
Mots-clésProduction & Quality Control Management; Production Systems; Operations Management
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