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Improving the Genetic Algorithms Performance in Simple Assembly Line Balancing

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Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3984))

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Abstract

In this paper, a hybrid GA approach combining genetic algorithm (GA) and tabu search (TS) is proposed to solve simple assembly line balancing problem. As this problem is combinatorial and NP hard in nature, the optimum seeking methods are impractical. Therefore, we proposed a hybrid approach, which unites the advantages and mitigates the disadvantages of the two algorithms. To increase the performance of the hybrid GA, we also optimized the control parameters such as the population size, rate of crossover and mutation. Moreover, to gain more insight on the performance of hybrid GA, we implemented it to various benchmark problems and observed that the hybridization of GA with TS improves the solution performance of the balancing problem.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Dokuz Eylul University, 35100, Bornova, Izmir, Turkey

    Seren Özmehmet Tasan & Semra Tunali

Authors
  1. Seren Özmehmet Tasan
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  2. Semra Tunali
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary,, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd.,, Cathedral House 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

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© 2006 Springer-Verlag Berlin Heidelberg

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Tasan, S.Ö., Tunali, S. (2006). Improving the Genetic Algorithms Performance in Simple Assembly Line Balancing. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751649_9

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  • DOI: https://doi.org/10.1007/11751649_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34079-9

  • Online ISBN: 978-3-540-34080-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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