An evolutionary fuzzy multi-objective approach to cell formation

Authors:
Chang-Chun Tsai;Chao-Hsien Chu;Xiaodan Wu
Affiliations:
Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan;College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA;School of Management, Hebei University of Technology, Tianjin, China
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

Citing 2
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Manufacturing Systems Design

Genetic Algorithms and Manufacturing Systems Design

A simulation-based evolutionary multiobjective approach to manufacturing cell formation

Computers and Industrial Engineering

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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.