An evolutionary fuzzy multi-objective approach to cell formation

Chang Chun Tsai, Chao Hsien Chu, Xiaodan Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationSimulated Evolution and Learning - 6th International Conference, SEAL 2006, Proceedings
PublisherSpringer Verlag
Number of pages7
ISBN (Print)3540473319, 9783540473312
StatePublished - Jan 1 2006
Event6th International Conference Simulated Evolution and Learning, SEAL 2006 - Hefei, China
Duration: Oct 15 2006Oct 18 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4247 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th International Conference Simulated Evolution and Learning, SEAL 2006

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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