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An Evolutionary Fuzzy Multi-objective Approach to Cell Formation

  • Chang-Chun Tsai
  • Chao-Hsien Chu
  • Xiaodan Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Goal Programming Cellular Manufacture Cell Formation Problem Fuzzy Mathematical Programming Seed Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chang-Chun Tsai
    • 1
  • Chao-Hsien Chu
    • 2
  • Xiaodan Wu
    • 3
  1. 1.Department of Industrial and Information ManagementNational Cheng Kung UniversityTainanTaiwan
  2. 2.College of Information Sciences and TechnologyThe Pennsylvania State UniversityUniversity ParkUSA
  3. 3.School of ManagementHebei University of TechnologyTianjinChina

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