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Advances in Multiobjective Hybrid Genetic Algorithms for Intelligent Manufacturing and Logistics Systems

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Active Media Technology (AMT 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8210))

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Abstract

Recently, genetic algorithms (GA) have received considerable attention regarding their potential as a combinatorial optimization for complex problems and have been successfully applied in the area of various engineering. We will survey recent advances in hybrid genetic algorithms (HGA) with local search and tuning parameters and multiobjective HGA (MO-HGA) with fitness assignments. Applications of HGA and MO-HGA will introduced for flexible job-shop scheduling problem (FJSP), reentrant flow-shop scheduling (RFS) model, and reverse logistics design model in the manufacturing and logistics systems.

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Author information

Authors and Affiliations

  1. Fuzzy Logic Systems Institute, Iizuka, Japan

    Mitsuo Gen

  2. National Tsing Hua University, Taiwan

    Mitsuo Gen

  3. Maebashi Institute of Technology, Maebashi, Japan

    Kenichi Ida

Authors
  1. Mitsuo Gen
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  2. Kenichi Ida
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Editor information

Editors and Affiliations

  1. Graduate School of Information Science and Technology, Hokkaido University, N-14 W-9, 060-0814, Sapporo, Japan

    Tetsuya Yoshida

  2. Southwestern University of Finance and Economics, Chengdu, China

    Gang Kou

  3. Institute of Mathematics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Hong Kong Polytechnic University, Kowloon, Hong Kong

    Jiannong Cao

  5. Bell Labs, Paris, France

    Hakim Hacid

  6. Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-cho, 371-0816, Maebashi, Gunma, Japan

    Ning Zhong

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© 2013 Springer International Publishing Switzerland

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Gen, M., Ida, K. (2013). Advances in Multiobjective Hybrid Genetic Algorithms for Intelligent Manufacturing and Logistics Systems. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds) Active Media Technology. AMT 2013. Lecture Notes in Computer Science, vol 8210. Springer, Cham. https://doi.org/10.1007/978-3-319-02750-0_41

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  • DOI: https://doi.org/10.1007/978-3-319-02750-0_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02749-4

  • Online ISBN: 978-3-319-02750-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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