, Volume 17, Issue 2–3, pp 183–191 | Cite as

A genetic search algorithm to optimize job sequencing under a technological constraint in a rolling-mill facility

  • Étienne Mayrand
  • Pierre Lefrançois
  • Ossama Kettani
  • Marie -Hélène Jobin


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.

Key words

Genetic search algorithm job scheduling sequence dependent setup costs 


Betrachtet wird ein dynamisches Problem der Reihenfolgeplanung in einem Walzwerk. Ziel ist die Minimierung der Summe aus Lagerkosten für Halbfertigfabrikate und reihenfolgeabhängigen Rüstkosten. Zur Lösung wird ein genetischer Algorithmus benutzt. Zur Beurteilung der Leistungsfähigkeit des Verfahrens werden für kleinere Probleme exakte Lösungen herangezogen, für größere Probleme erfolgt ein Vergleich mit prioritätsregelbasierten Verfahren.


Genetische Algorithmen Maschinenbelegungsplanung reihenfolgeabhängige Rüstkosten 


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Copyright information

© Springer-Verlag 1995

Authors and Affiliations

  • Étienne Mayrand
    • 1
  • Pierre Lefrançois
    • 1
  • Ossama Kettani
    • 1
  • Marie -Hélène Jobin
    • 2
  1. 1.SORCIIER Research Center on the International Competitiveness and the Engineering of the Networked Enterprise, Faculté des sciences de l'administrationUniversité LavalSte-FoyCanada
  2. 2.Service de l'enseignement de la productionHautes Études CommercialesMontréalCanada

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