A hybrid single and dual population search procedure for the job shop scheduling problem

V. Sels (), K. Craeymeersch and Mario Vanhoucke

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: This paper presents a genetic algorithm and a scatter search procedure to solve the well-known job shop scheduling problem. In contrast to the single population search performed by the genetic algorithm, the scatter search algorithm splits the population of solutions in a diverse and high-quality set to exchange information between individuals in a controlled way. Extensions from a single to a dual population by taking problem specific characteristics into account can be seen as a stimulator to add diversity in the search process, which has a positive influence on the important balance between intensification and diversification. Computational experiments verify the benefit of this diversity on the effectiveness of the meta-heuristic search process. Various algorithmic parameters from literature are embedded in both procedures and a detailed comparison is made. A set of standard instances is used to compare the different approaches and the best obtained results are benchmarked against heuristic solutions found in literature.

Pages: 2 pages
Date: 2010-09
New Economics Papers: this item is included in nep-cmp
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http://wps-feb.ugent.be/Papers/wp_10_679.pdf (application/pdf)

Related works:
Journal Article: A hybrid single and dual population search procedure for the job shop scheduling problem (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:rug:rugwps:10/679

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