Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information
The purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggre...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/2841325 |
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doaj-a4a660d778624e1e9105e016cf4309032020-11-24T23:10:44ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/28413252841325Simultaneous Hierarchical Clustering for Cell Formation Problems with Production InformationYingyu Zhu0Simon Li1Department of Mechanical and Manufacturing Engineering, University of Calgary, Alberta, CanadaDepartment of Mechanical and Manufacturing Engineering, University of Calgary, Alberta, CanadaThe purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a two-mode similarity coefficient is applied to simultaneously form machine groups and part families based on the classical framework of hierarchical clustering. This practice not only eliminates the sequential process of grouping machines (or parts) first and then assigning parts (or machines), but also improves the quality of solutions. The proposed clustering method has been tested through twelve literature examples. The results demonstrate that the proposed method can at least yield solutions comparable to the solutions obtained by metaheuristics. It can yield better results in some instances, as well.http://dx.doi.org/10.1155/2018/2841325 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yingyu Zhu Simon Li |
spellingShingle |
Yingyu Zhu Simon Li Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information Mathematical Problems in Engineering |
author_facet |
Yingyu Zhu Simon Li |
author_sort |
Yingyu Zhu |
title |
Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information |
title_short |
Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information |
title_full |
Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information |
title_fullStr |
Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information |
title_full_unstemmed |
Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information |
title_sort |
simultaneous hierarchical clustering for cell formation problems with production information |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
The purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a two-mode similarity coefficient is applied to simultaneously form machine groups and part families based on the classical framework of hierarchical clustering. This practice not only eliminates the sequential process of grouping machines (or parts) first and then assigning parts (or machines), but also improves the quality of solutions. The proposed clustering method has been tested through twelve literature examples. The results demonstrate that the proposed method can at least yield solutions comparable to the solutions obtained by metaheuristics. It can yield better results in some instances, as well. |
url |
http://dx.doi.org/10.1155/2018/2841325 |
work_keys_str_mv |
AT yingyuzhu simultaneoushierarchicalclusteringforcellformationproblemswithproductioninformation AT simonli simultaneoushierarchicalclusteringforcellformationproblemswithproductioninformation |
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1725605723526660096 |