@inproceedings{b92ab65adb41429b8ce6108f826ab76d,
title = "An evolutionary fuzzy multi-objective approach to cell formation",
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.",
author = "Tsai, {Chang Chun} and Chu, {Chao Hsien} and Xiaodan Wu",
year = "2006",
month = jan,
day = "1",
doi = "10.1007/11903697_48",
language = "English (US)",
isbn = "3540473319",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "377--383",
booktitle = "Simulated Evolution and Learning - 6th International Conference, SEAL 2006, Proceedings",
address = "Germany",
note = "6th International Conference Simulated Evolution and Learning, SEAL 2006 ; Conference date: 15-10-2006 Through 18-10-2006",
}