A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem

Abstract : This paper considers the job-shop problem with release dates and due dates, with the objective of minimizing the total weighted tardiness. A genetic algorithm is combined with an iterated local search that uses a longest path approach on a disjunctive graph model. A design of experiments approach is employed to calibrate the parameters and operators of the algorithm. Previous studies on genetic algorithms for the job-shop problem point out that these algorithms are highly depended on the way the chromosomes are decoded. In this paper, we show that the efficiency of genetic algorithms does no longer depend on the schedule builder when an iterated local search is used. Computational experiments carried out on instances of the literature show the efficiency of the proposed algorithm.
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Journal articles
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https://hal-emse.ccsd.cnrs.fr/emse-00520349
Contributor : Stéphane Dauzère-Pérès <>
Submitted on : Wednesday, September 22, 2010 - 11:43:45 PM
Last modification on : Monday, February 25, 2019 - 10:26:08 AM

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Imen Essafi, Yazid Mati, Stéphane Dauzère-Pérès. A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem. Computers and Operations Research, Elsevier, 2008, 35 (8), pp.2599-2616. ⟨10.1016/j.cor.2006.12.019⟩. ⟨emse-00520349⟩

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