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Parallel Genetic Algorithm for a Flow-Shop Problem with Multiprocessor Tasks

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Computational Science and Its Applications — ICCSA 2003 (ICCSA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2667))

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Abstract

Machine scheduling problems belong to the most difficult deterministic combinatorial optimization problems. Hence, most scheduling problems are NP-hard and it is impossible to find the optimal schedule in reasonable time. In this paper, we consider a flow-shop scheduling problem with multi-processor tasks. A parallel genetic algorithm using multithreaded programming technique is developed to obtain a quick but good solution to the problem. The performance of the parallel genetic algorithm under various conditions and parameters are studied and presented.

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Author information

Authors and Affiliations

  1. Dept. of Management, The Hong Kong Polytechnic, University, Hong Kong, SAR

    Ceyda Oğuz & X. T. Qi

  2. Dept. of Electrical Eng., The Hong Kong Polytechnic, University, Hong Kong, SAR

    Yu-Fai Fung

  3. School of Electrical and Electronic Eng., Singapore Polytechnic, Singapore

    M. Fikret Ercan

Authors
  1. Ceyda Oğuz
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  2. Yu-Fai Fung
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  3. M. Fikret Ercan
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  4. X. T. Qi
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Editor information

Editors and Affiliations

  1. Army High Performance Computing Research Center, USA

    Vipin Kumar

  2. Department of Computer Science, University of Calgary, Calgary, AB, T2N1N4, Canada

    Marina L. Gavrilova

  3. Heuchera Technologies Inc., 122 9251-8 Yonge Street, Richmond Hill, ON, Canada, L4C 9T3

    Chih Jeng Kenneth Tan

  4. Département d’informatique et de recherche opérationelle, Université de Montréal, Montréal, Québec, H3C 3J7, Canada

    Pierre L’Ecuyer

  5. Department of Computer Science and Engineering, University of Minessota, MN, 55455, USA

    Vipin Kumar

  6. The Queen’s University of Belfast, School of Computer Science, Belfast BT7 1NN, Northern Ireland, UK

    Chih Jeng Kenneth Tan

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© 2003 Springer-Verlag Berlin Heidelberg

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Oğuz, C., Fung, YF., Ercan, M.F., Qi, X.T. (2003). Parallel Genetic Algorithm for a Flow-Shop Problem with Multiprocessor Tasks. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds) Computational Science and Its Applications — ICCSA 2003. ICCSA 2003. Lecture Notes in Computer Science, vol 2667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44839-X_104

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  • DOI: https://doi.org/10.1007/3-540-44839-X_104

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40155-1

  • Online ISBN: 978-3-540-44839-6

  • eBook Packages: Springer Book Archive

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