An Evolutionary Fuzzy Multi-Objective Approach to Cell Formation
Publication Type
Conference Proceeding Article
Publication Date
5-2006
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.
Discipline
Computer Sciences | Management Information Systems
Publication
Simulated Evolution and Learning: 6th International Conference, SEAL 2006, Hefei, China, October 15-18, 2006. Proceedings
Volume
4247
First Page
377
Last Page
383
ISBN
9783540473312
Identifier
10.1007/11903697_48
Publisher
Springer Verlag
City or Country
Hefei, China
Citation
TSAI, C. C.; CHU, Chao-Hsien; and Wu, Xindong.
An Evolutionary Fuzzy Multi-Objective Approach to Cell Formation. (2006). Simulated Evolution and Learning: 6th International Conference, SEAL 2006, Hefei, China, October 15-18, 2006. Proceedings. 4247, 377-383.
Available at: https://ink.library.smu.edu.sg/sis_research/568
Additional URL
http://dx.doi.org/10.1007/11903697_48