An Evolutionary Fuzzy Multi-Objective Approach to Cell Formation
Conference Proceeding Article
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.
Computer Sciences | Management Information Systems
Information Systems and Management; Intelligent Systems and Decision Analytics
Simulated Evolution and Learning: 6th International Conference, SEAL 2006, Hefei, China, October 15-18, 2006. Proceedings
City or Country
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/568