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Metaheuristics for Solving a Hybrid Flexible Flowshop Problem with Sequence-Dependent Setup Times

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Swarm Intelligence Based Optimization (ICSIBO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8472))

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Abstract

In this paper, we propose three new metaheuristic implementations to address the problem of minimizing the makespan in a hybrid flexible flowshop with sequence-dependent setup times. The first metaheuristic is a genetic algorithm (GA) embedding two new crossover operators, and the second is an ant colony optimization (ACO) algorithm which incorporates a transition rule featuring lookahead information and past information based on archive concepts such as the multiobjective evolutionary computation. The third metaheuristic is a hybridization (HGA) of the GA and the ACO algorithms. Numerical experiments were performed to compare the performance of the proposed algorithms on different benchmarks from the literature. The algorithms are compared with the best algorithms from the literature. The results indicate that our algorithms generate better solutions than those of the known reference sets.

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Author information

Authors and Affiliations

  1. Département d’informatique et de mathématique, Université du Québec à Chicoutimi, 555 Boulevard Université, Chicoutimi, Québec, G7H 2B1, Canada

    Aymen Sioud, Caroline Gagné & Marc Gravel

Authors
  1. Aymen Sioud
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  2. Caroline Gagné
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  3. Marc Gravel
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Corresponding author

Correspondence to Aymen Sioud .

Editor information

Editors and Affiliations

  1. Univ Paris-Est Créteil UPEC LISSI, Vitry Sur Seine, France

    Patrick Siarry

  2. Université de Haute Alsace LMIA-INRIA Grand Est, Mulhouse, France

    Lhassane Idoumghar

  3. Université de Haute-Alsace, LMIA, Mulhouse, France

    Julien Lepagnot

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© 2014 Springer International Publishing Switzerland

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Sioud, A., Gagné, C., Gravel, M. (2014). Metaheuristics for Solving a Hybrid Flexible Flowshop Problem with Sequence-Dependent Setup Times. In: Siarry, P., Idoumghar, L., Lepagnot, J. (eds) Swarm Intelligence Based Optimization. ICSIBO 2014. Lecture Notes in Computer Science(), vol 8472. Springer, Cham. https://doi.org/10.1007/978-3-319-12970-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-12970-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12969-3

  • Online ISBN: 978-3-319-12970-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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