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A Hybrid Genetic Algorithm for the Flow-Shop Scheduling Problem

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Advances in Applied Artificial Intelligence (IEA/AIE 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4031))

Abstract

The flow-shop scheduling problem with the makespan criterion is an important production scheduling problem. Although this problem has a simple formulation, it is NP-hard. Therefore many heuristic and metaheuristic methods had been proposed to solve this problem. In this paper, a hybrid genetic algorithm is presented for the flow-shop scheduling problem. In our method, a modified version of NEH with random re-start is used to generate the initial population. Also, a new orthogonal array crossover is devised as the crossover operator of the genetic algorithm. The tabu search is hybridized with the genetic algorithm and acts as the local search method. The proposed algorithm had been tested on two benchmarks. The results are compared with those of other methods that had also been tested on these benchmarks. The comparison shows that our method outperforms other methods on these benchmarks.

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Author information

Authors and Affiliations

  1. Department of Computer Science, National Chung Hsing University, 250 Kuo Kuang Road, Taichung, Taiwan

    Lin-Yu Tseng & Ya-Tai Lin

Authors
  1. Lin-Yu Tseng
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  2. Ya-Tai Lin
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University-San Marcos, Nueces 247, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

  2. ESIA Laboratoire d’Informatique, Sytèmes, Traitement de l’Information et de la Connaissance, Université de Savoie, B.P. 806, F-74016, ANNECY Cedex, France

    Richard Dapoigny

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© 2006 Springer-Verlag Berlin Heidelberg

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Tseng, LY., Lin, YT. (2006). A Hybrid Genetic Algorithm for the Flow-Shop Scheduling Problem. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_25

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  • DOI: https://doi.org/10.1007/11779568_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35453-6

  • Online ISBN: 978-3-540-35454-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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