Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/10673
Authors: Rathour, Sanjeev S.
Issue Date: 2000
Abstract: Cell formation is an important issue in the design and implementation of a Cellular Manufacturing System. In cellular manufacturing, parts with similar manufacturing requirements are identified and grouped into part families The machines required to process a particular part family are grouped together and organized into machine cells. Thus machine cells are groups of functionally dissimilar machines dedicated to the production of a particular part family. The similarity coefficient methods of cell formation first group machines into cells based on a measure of similarity defined between machines. Subsequently parts are assigned to appropriate machine cells based on their routings. These methods are more flexible in incorporating various types of production related data into the machine cell formation process and thus arrive at more realistic solutions to the problem of identifying feasible machine groups and part families. The Similarity Coefficient Methods are thus capable of becoming more closely linked to the objectives associated with adopting cellular manufacturing system. In this work the various types of similarity coefficients that have evolved over a period of time have been discussed. A comparison has been made of the methods based on the information contained in machine- part matrix only such as Rank Order Clustering with the Similarity Coefficient Methods. The author has developed computer software in C++ language for the Rank Order Clustering and for Similarity Coefficient Method. The software for the Similarity Coefficient Method has the following characteristic features: (iii) i) It is capable of processing initial machine- part matrix containing ordinal data. The earlier methods were capable of processing matrices based on binary data (0 and 1) only. ii) It is capable of incorporating the effect of various production parameters such as part volume, material handling costs and multiple visits by a part to a machine etc, in the machine- part grouping process. iii) The algorithm provides a number of alternative solutions to the user at various levels of similarity (threshold value) defined among machines. The threshold value is calculated intrinsically by the program. iv) The algorithm attempts to minimize the number of inter-cellular moves during the cell formation process v) Assuming a single row intra cellular layout of machines, the algorithm arranges of machines within a cell within a cell so as to minimize the distances traveled by the parts. Besides the Similarity Coefficient Methods, the other methods of cell formation have also been discussed in brief. The software has been tested for numerous problem data sets as illustrated in chapter 6.
Other Identifiers: M.Tech
Research Supervisor/ Guide: Shan, H. S.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' THESES (MIED)

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