Journal Article10.1080/00207548908942674
Commonality analysis: A linear cell clustering algorithm for group technology
Jerry C. Wei,Gary M. Kern +1 more
120
TL;DR: In this paper, the authors present a linear clustering algorithm based on the calculation of a commonality score which indicates the similarity in the way two machines are used in the shop to manufacture the products or parts.
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Abstract: Numerous researchers have suggested methods for clustering machines into manufacturing cells in a Group Technology environment. Many of these methods are numerically complex. This paper presents a new linear clustering algorithm that is fast, simple and quite flexible. The algorithm is based on the calculation of a commonality score which indicates the similarity in the way two machines are used in the shop to manufacture the products or parts.
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Citations
Cell formation in group technology: review, evaluation and directions for future research
TL;DR: In this article, the authors provide a comprehensive mathematical formulation of the cell formation problem and then propose a methodology-based classification of prior research, based on a comparison and critical evaluation, highlight the shortcomings of current approaches and also outline directions for future research.
450
Combining lean and agile characteristics: Creation of virtual groups by enhanced production flow analysis
TL;DR: The background to why some manufacturing organisations require a combination of agile and lean characteristics in their manufacturing organisations is presented and the development of the virtual group (VG) concept is described, which is the application of virtual cells to functional layouts.
179
Machine-component cell formation in group technology : a neural network approach
TL;DR: In this paper, a neural network clustering method for the part-machine grouping problem in group technology is presented, which utilizes binary-valued inputs and it can be trained without supervision.
154
A systematic approach for product families formation in Reconfigurable Manufacturing Systems
TL;DR: In this paper, a methodology for grouping products into families, which takes into account the requirements of products in RMSs, is presented, based on the average linkage clustering algorithm.
133
Cellular manufacturing: A taxonomic review framework
TL;DR: In this article, the authors employ a taxonomic framework for a comprehensive review of cellular manufacturing systems, including visual inspection, part coding and classification, and the analysis of the production process.
116
References
Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm
TL;DR: In this paper, a new approach using a rank order clustering algorithm is described which is particularly relevant to the problem of machine-component group formation, and a relaxation and regrouping procedure is developed whereby the basic rank-order clustering method may be extended to the case where there are bottleneck machines.
831
Machine-component group formation in group technology: review and extension
J. R. King,V. Nakornchai +1 more
TL;DR: A comprehensive review of the various approaches that have been adopted in an attempt to solve the problem of forming machines into groups and components into associated families in Group Technology can be found in this article.
722
The generalized group technology concept
TL;DR: In this article, a generalized group technology concept, based on generation for one part of a number of different process plans, is proposed, which improves the quality of process (part) families and machine cells.
584
Efficient solving of the group technology problem
Andrew Kusiak,Wing S. Chow +1 more
TL;DR: In this paper, the authors considered the standard and augmented group technology problem, and proposed two algorithms to solve the two problems: the standard formulation and the augmented formulation, which allows the creation of machine cells and part families with a low degree of interaction by removing parts with low values of corresponding costs from the incidence matrix.
390