Single linkage versus average linkage clustering in machine cells formation applications

Seifoddini, Hamid

doi:10.1016/0360-8352(89)90160-5

Cited by 180 publications

(62 citation statements)

References 12 publications

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“…Since the basic idea of GT depends on the estimation of the similarities between part pairs and cluster analysis is the most basic method for estimating similarities, it is concluded that SCM based method is one of the most basic methods for solving CF problems. Despite previous studies (Seifoddini, 1989a) indicated that SCM based approaches are more flexible in incorporating manufacturing data into the machine-cells formation process, none of the previous articles has explained the reason why SCM based methods are more flexible than other approaches such as MP and HA. We try to explain the reason as follows.…”

Section: Similarity Coefficient Methods (Scm)mentioning

confidence: 72%

“…Largest tendency to chain; Leads to the lowest densities and the highest degree of single part cells (Seifoddini, 1989a;Gupta, 1991;Vakharia & Wemmerlöv, 1995).…”

Section: Slcmentioning

confidence: 99%

“…Performed as the best procedure; Produces the lowest degree of chaining; Leads to the highest cell densities; Indifferent to choice of similarity coefficients; Few single part cells (Tarsuslugil & Bloor, 1979;Seifoddini, 1989a;Vakharia & Wemmerlöv, 1995;Yasuda & Yin, 2001).…”

Section: Alcmentioning

confidence: 99%

“…Alternatively, the total volume for a family of similar parts may be enough to efficiently utilize a machine-cell (Miltenburg and Zhang, 1991). It has been reported (Seifoddini, 1989a) that employing CM may help overcome major problems of batch-type manufacturing including frequent setups, excessive in-process inventories, long through-put times, complex planning and control functions, and provides the basis for implementation of manufacturing techniques such as just-in-time (JIT) and flexible manufacturing systems (FMS).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application Similarity Coefficient Method to Cellular Manufacturing

Yin¹

2006

Manufacturing the Future

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Section: Similarity Coefficient Methods (Scm)mentioning

confidence: 72%

“…Largest tendency to chain; Leads to the lowest densities and the highest degree of single part cells (Seifoddini, 1989a;Gupta, 1991;Vakharia & Wemmerlöv, 1995).…”

Section: Slcmentioning

confidence: 99%

Section: Alcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application Similarity Coefficient Method to Cellular Manufacturing

Yin¹

2006

Manufacturing the Future

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“…In this paper, the average-linkage algorithm in hierarchical clustering [53] is applied to CSM in order to build the tree with both machines and parts. Taking the CSM as the input, the tree is constructed based on the similarity values between any two objects.…”

Section: Methodical Proceduresmentioning

confidence: 99%

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Zhu

2018

Mathematical Problems in Engineering

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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 twomode 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.

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An effective Sorensen‐single linkage clustering hybrid algorithm for cell formation problems in cellular manufacturing industry

Sathish¹,

Karuppuswamy³

et al. 2019

Concurrency and Computation

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Summary The paper presented here entails technique that is based upon Sorensen's similarity coefficient as well as the Sorensen‐ Single Linkage Clustering (SLC) algorithm, which essentially is a hybrid clustering approach that has been deployed here to show issue related to cell formation during the process of cellular engineering. In the following paper and study that has been presented here, the suggested hybrid algorithm comparison has been drawn versus the existing clustering algorithms and their performances have been compared specifically the MOD‐SLC modified single linkage or SLC also known as the single linkage clustering algorithm. The Sorensen‐SLC technique and the inferences drawn on the same show that it performs comparatively better when compared with the MOD‐SLC and SLC algorithms. Additionally, the computation that is necessary for this hybrid algorithm is absolutely a bare minimal and the computation processing is effective and easy.

show abstract

Single linkage versus average linkage clustering in machine cells formation applications

Cited by 180 publications

References 12 publications

Application Similarity Coefficient Method to Cellular Manufacturing

Application Similarity Coefficient Method to Cellular Manufacturing

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

An effective Sorensen‐single linkage clustering hybrid algorithm for cell formation problems in cellular manufacturing industry

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