CASE: A clustering algorithm for cell formation with sequence data

Nair, G. Jayakrishnan; Narendran, T.T.

doi:10.1080/002075498193985

Cited by 139 publications

(93 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Total number of 1 s outside the diagonal block in clustered matrix, N 0 in is the Total number of 0 s inside the diagonal block of clustered matrix (Albadawi et al 2005;Nair and Narendran 1998;Sarker 1996;Wang 2003). GI ?…”

Section: Comparison Of Results Of Clustering Algorithm Usedmentioning

confidence: 99%

“…grouping efficiency: It incorporates both machine utilization and inter-cell movement. It is defined as the sum of two weighted functions, one for machine utilization and other for inter-cell movement with a weight factor; recommendation is in favour of equal weight for both functions (Chandrasekharan and Rajagopalan 1986;Nair and Narendran 1998;Sarker 1996;Wang 2003).…”

Section: Comparison Of Results Of Clustering Algorithm Usedmentioning

confidence: 99%

See 1 more Smart Citation

Development of a cell formation heuristic by considering realistic data using principal component analysis and Taguchi’s method

Kumar

Sharma

2014

J Ind Eng Int

View full text Add to dashboard Cite

Over the last four decades of research, numerous cell formation algorithms have been developed and tested, still this research remains of interest to this day. Appropriate manufacturing cells formation is the first step in designing a cellular manufacturing system. In cellular manufacturing, consideration to manufacturing flexibility and productionrelated data is vital for cell formation. The consideration to this realistic data makes cell formation problem very complex and tedious. It leads to the invention and implementation of highly advanced and complex cell formation methods. In this paper an effort has been made to develop a simple and easy to understand/implement manufacturing cell formation heuristic procedure with considerations to the number of production and manufacturing flexibility-related parameters. The heuristic minimizes inter-cellular movement cost/time. Further, the proposed heuristic is modified for the application of principal component analysis and Taguchi's method. Numerical example is explained to illustrate the approach. A refinement in the results is observed with adoption of principal component analysis and Taguchi's method.

show abstract

Section: Comparison Of Results Of Clustering Algorithm Usedmentioning

confidence: 99%

Section: Comparison Of Results Of Clustering Algorithm Usedmentioning

confidence: 99%

Development of a cell formation heuristic by considering realistic data using principal component analysis and Taguchi’s method

Kumar

Sharma

2014

J Ind Eng Int

View full text Add to dashboard Cite

show abstract

“…To extend the generalized similarity by Won and Kim [25], Yin and Yasuda [26] developed the formulation of sequence ratio (SR) that compares the actual and theoretically highest intermachine movements between two machines. Nair and Narendran [3] focused on the intermediate operations that will incur two intercell movements as compared to the first and last operations. This similarity notion was also adopted by Won and Lee [27].…”

Section: Similarity Coefficients In Cfmentioning

confidence: 99%

“…Particularly, if an operation is in the middle and the relevant part and machine are not in the same cell, this part needs to move out and then move in with respect to its own cell (i.e., 2 times). Let = [ ] be the weighted sequence matrix [3] to capture this idea, and it is defined as follows:…”

Section: Measures Of Solution Qualitymentioning

confidence: 99%

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Zhu

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

“…In this regard, many models and solution approaches have been developed to identify machine cells and part families. These approaches can be classified into three main categories [4]: 1) mathematical programming (MP) models [5][6][7][8], 2) heuristic/meta-heuristic solution algorithms [9][10][11], and 3) similarity coefficient methods (SCM) [12,13].…”

Section: Introductionmentioning

confidence: 99%

Development of a Web-Based Decision Support System for Cell Formation Problems Considering Alternative Process Routings and Machine Sequences

Chang¹

2010

JSEA

View full text Add to dashboard Cite

show abstract

CASE: A clustering algorithm for cell formation with sequence data

Cited by 139 publications

References 0 publications

Development of a cell formation heuristic by considering realistic data using principal component analysis and Taguchi’s method

Development of a cell formation heuristic by considering realistic data using principal component analysis and Taguchi’s method

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Development of a Web-Based Decision Support System for Cell Formation Problems Considering Alternative Process Routings and Machine Sequences

Contact Info

Product

Resources

About