A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation
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 applica...
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sg-smu-ink.sis_research-12202010-09-24T05:42:03Z A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation TSAI, C.C. Chu, Chao-Hsien 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. 2006-04-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/221 info:doi/10.1007/11903697_48 http://dx.doi.org/10.1007/11903697_48 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics |
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Artificial Intelligence and Robotics TSAI, C.C. Chu, Chao-Hsien A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation |
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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. |
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TSAI, C.C. Chu, Chao-Hsien |
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TSAI, C.C. Chu, Chao-Hsien |
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TSAI, C.C. |
title |
A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation |
title_short |
A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation |
title_full |
A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation |
title_fullStr |
A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation |
title_full_unstemmed |
A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation |
title_sort |
fuzzy multi-objective linear programming model for manufacturing cell formation |
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Institutional Knowledge at Singapore Management University |
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2006 |
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https://ink.library.smu.edu.sg/sis_research/221 http://dx.doi.org/10.1007/11903697_48 |
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