A Fuzzy Clustering Approach to Manufacturing Cell Formation
Cell formation, one of the most important problems faced in designing cellular manufacturing systems, is to group parts with similar geometry, function, material and process into part families and the corresponding machines into machine cells. There has been an extensive amount of work in this area...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
1991
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/1796 http://dx.doi.org/10.1080/00207549108948024 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Cell formation, one of the most important problems faced in designing cellular manufacturing systems, is to group parts with similar geometry, function, material and process into part families and the corresponding machines into machine cells. There has been an extensive amount of work in this area and, consequently, numerous analytical approaches have been developed. One common weakness of these conventional approaches is that they implicitly assume that disjoint part families exist in the data; therefore, a part can only belong to one part family. In practice, it is clear that some parts definitely belong to certain part families, whereas there exist parts that may belong to more than one family. In this study, we propose a fuzzy c-means clustering algorithm to formulate the problem. The fuzzy approach offers a special advantage over conventional clustering. It not only reveals the specific part family that a part belongs to, but also provides the degree of membership of a part associated with each part family. This information would allow users flexibility in determining to which part family a part should be assigned so that the workload balance among machine cells can be taken into consideration. We have also developed a computer program to simplify the implementation and to study the impact of the model's parameters on the clustering results. |
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