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dc.contributor.authorJeunet, Jully
dc.contributor.authorDellaert, Nico
dc.date.accessioned2009-09-14T09:24:42Z
dc.date.available2009-09-14T09:24:42Z
dc.date.issued2000
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/1577
dc.language.isoenen
dc.subjectProduction & Quality Control Managementen
dc.subjectProduction Systemsen
dc.subjectOperations Managementen
dc.subject.ddc003en
dc.titleSolving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithmen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe 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.en
dc.relation.isversionofjnlnameInternational Journal of Production Research
dc.relation.isversionofjnlvol38en
dc.relation.isversionofjnlissue5en
dc.relation.isversionofjnldate2000
dc.relation.isversionofjnlpages1083-1099en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherTaylor & Francisen
dc.subject.ddclabelRecherche opérationnelleen


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