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A Hybrid Dual-Population Genetic Algorithm for the Single Machine Maximum Lateness Problem

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6622))

Abstract

We consider the problem of scheduling a number of jobs, each job having a release time, a processing time and a due date, on a single machine with the objective of minimizing the maximum lateness. We developed a hybrid dual-population genetic algorithm and compared its performance with alternative methods on a new diverse data set. 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. Based on a comprehensive literature study on genetic algorithms in single machine scheduling, a fair comparison of genetic operators was made.

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Author information

Authors and Affiliations

  1. Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000, Gent, Belgium

    Veronique Sels & Mario Vanhoucke

  2. Operations and Technology Management Centre, Vlerick Leuven Gent Management School, Reep 1, 9000, Gent, Belgium

    Mario Vanhoucke

Authors
  1. Veronique Sels
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  2. Mario Vanhoucke
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Editor information

Editors and Affiliations

  1. Department of Business Administration and Computer Science, University of Applied Sciences and Arts, Ricklinger Stadtweg 120, 30459, Hannover, Germany

    Peter Merz

  2. Faculty of Sciences, University of Angers, 2, Boulevard Lavoisier, Cedex 01, 49045, Angers, France

    Jin-Kao Hao

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© 2011 Springer-Verlag Berlin Heidelberg

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Sels, V., Vanhoucke, M. (2011). A Hybrid Dual-Population Genetic Algorithm for the Single Machine Maximum Lateness Problem. In: Merz, P., Hao, JK. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2011. Lecture Notes in Computer Science, vol 6622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20364-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-20364-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20363-3

  • Online ISBN: 978-3-642-20364-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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