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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2006: Simulated Evolution and Learning pp 377–383Cite as

An Evolutionary Fuzzy Multi-objective Approach to Cell Formation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

Abstract

Fuzzy mathematical programming (FMP) has been shown not only providing a better and more flexible way of representing the cell formation (CF) problem of cellular manufacturing, but also improving solution quality and computational efficiency. However, FMP cannot meet the demand of real-world applications because it can only be used to solve small-size problems. In this paper, we propose a heuristic genetic algorithm (HGA) as a viable solution for solving large-scale fuzzy multi-objective CF problems. Heuristic crossover and mutation operators are developed to improve computational efficiency. Our results show that the HGA outperforms the FMP and goal programming (GP) models in terms of clustering results, computational time, and user friendliness.

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Author information

Authors and Affiliations

  1. Department of Industrial and Information Management, National Cheng Kung University, Tainan, 701, Taiwan

    Chang-Chun Tsai

  2. College of Information Sciences and Technology, The Pennsylvania State University, 301K IST Building, University Park, PA, 16802, USA

    Chao-Hsien Chu

  3. School of Management, Hebei University of Technology, Tianjin, 300130, China

    Xiaodan Wu

Authors
  1. Chang-Chun Tsai
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  2. Chao-Hsien Chu
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  3. Xiaodan Wu
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Tsai, CC., Chu, CH., Wu, X. (2006). An Evolutionary Fuzzy Multi-objective Approach to Cell Formation. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_48

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  • DOI: https://doi.org/10.1007/11903697_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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