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- W3143103660 abstract "This article presents some results from the application of a genetic search algorithm to solve a job scheduling problem where setup costs depend on the order of the jobs. An empirical study shows that, for small problems, the solutions given by the genetic algorithm are as good as those obtained with a mixed-integer linear program. For larger problems that are computationally infeasible, we benchmark the genetic solutions against traditional scheduling heuristics. We also study different population management strategies that can improve the performance of the algorithm. Finally, future research avenues are discussed. Zusammenfassung. Betrachtet wird ein dynamisches Problem der Reihenfolgeplanung in einem Walzwerk. Ziel ist die Minimierung der Summe aus Lagerkosten ftir Halbfertigfabrikate und reihenfolgeabh~ingigen Riistkosten. Zur L6sung wird ein genetischer Algorithmus benutzt. Zur Beurteilung der Leistungsffihigkeit des Verfahrens werden fiJr kleinere Probleme exakte L6sungen hcrangezogen, ftir gr613ere Probleme erfolgt ein Vergteich mit priorit~tsregelbasierten Verfahren." @default.
- W3143103660 created "2021-04-13" @default.
- W3143103660 creator A5029645364 @default.
- W3143103660 date "2005-01-01" @default.
- W3143103660 modified "2023-09-26" @default.
- W3143103660 title "A genetic search algorithm to optimize job sequencing under a technological constraint in a rolling-mill facility" @default.
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- W3143103660 hasPublicationYear "2005" @default.
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