Part-machine family formation using genetic algorithms in a fuzzy environment
In traditional cell-formation problems, it is assumed that each part is developed on a single machine. However, in many realistic situations, the suitability of alternative machines for developing the same part feature is frequently vague. Therefore, the fuzzy relation is ideally suited to represent...
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| Published in | International journal of advanced manufacturing technology Vol. 25; no. 11-12; pp. 1175 - 1179 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Springer Nature B.V
01.06.2005
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0268-3768 1433-3015 |
| DOI | 10.1007/s00170-003-1944-z |
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| Abstract | In traditional cell-formation problems, it is assumed that each part is developed on a single machine. However, in many realistic situations, the suitability of alternative machines for developing the same part feature is frequently vague. Therefore, the fuzzy relation is ideally suited to represent this vague and uncertain cell-formation problem. Furthermore, part-machine family formation problems are usually NP-complete. In this study, a genetic algorithm (GA) is used to deal with the problem. The aim of this study is to design a GA-based algorithm to solve the part-machine family formation problem in a fuzzy environment. The results of computational tests presented are very promising. |
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| AbstractList | In traditional cell-formation problems, it is assumed that each part is developed on a single machine. However, in many realistic situations, the suitability of alternative machines for developing the same part feature is frequently vague. Therefore, the fuzzy relation is ideally suited to represent this vague and uncertain cell-formation problem. Furthermore, part-machine family formation problems are usually NP-complete. In this study, a genetic algorithm (GA) is used to deal with the problem. The aim of this study is to design a GA-based algorithm to solve the part-machine family formation problem in a fuzzy environment. The results of computational tests presented are very promising. |
| Author | Chang, Ping-Teng Lee, Shi-Yin Pai, Ping-Feng |
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| CitedBy_id | crossref_primary_10_1016_j_cor_2010_02_007 crossref_primary_10_1007_s00170_012_4254_5 crossref_primary_10_1007_s00170_011_3413_4 crossref_primary_10_1007_s00170_013_5392_0 crossref_primary_10_1007_s00500_023_08632_9 crossref_primary_10_1016_j_cie_2012_09_016 crossref_primary_10_1155_2014_301751 |
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| Title | Part-machine family formation using genetic algorithms in a fuzzy environment |
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