( 2009) revealed that 80 % of manufacturing CF approaches are focussed on the arrangement of binary part-machine incidence matrix, whereas more realistic and effective approaches could be developed by considering the various manufacturing flexibility and production-related data (Kumar and Sharma 2014). A large number of cell CF approaches have been developed so far, majority of them do not consider production-related data (Boutsinas 2013 Won and Lee 2001). Amongst CF techniques similarity coefficient-based methods are more flexible and easy to implement (Yin and Yasuda 2006). 2005 Yin and Yasuda 2006) (i) Similarity coefficient-based methods (ii) Mathematical programming-based methods (iii) Artificial intelligence-based approaches (iv) Heuristics/meta-heuristics/hybrid meta-heuristics, (v) Any combination of these. 2013 Papaioannou and Wilson 2010 Kumar and Sharma 2014 Yasuda et al. The cell formation approaches developed so far can be categorised as (Boutsinas 2013 Lian et al. Therefore, a manufacturing CF approach should provide an optimisation amongst these, without much complexity in approach. It can simply be achieved by duplicating the machines but duplication of machine involves large capital investment which ultimately adds to the product cost. The essence of CF approaches is to eliminate/minimize the inter-cellular movement cost of parts (Arkat and Farahani 2012 Kumar and Sharma 2014 Lian et al. Ideally manufacturing cell is to be formed in such a fashion that each manufacturing cell should act as an independent manufacturing unit. Cell formation deals with the identification of the part families with similar process requirements and allocating them to the machine cells for processing (Boutsinas 2013 Fardis et al. 2009 Kumar and Sharma 2014) and key step (Krushinsky and Goldengorin 2012) in any cellular manufacturing problem. Amongst these, CF is the foremost (Doulabi et al. Cell formation (CF), group layout (GL) and group scheduling (GS) are the three major steps in cellular manufacturing (Fardis et al.
Due to its superior performance, it is considered as a feasible approach to realise mass customization philosophy (Lian et al. It has the ability to deal with frequent changes in product mix and fluctuations in production volume. Cellular manufacturing is a well-mixed blend of manufacturing flexibility and production efficiency. The concept of cellular manufacturing is placed at high level on the agenda of manufacturing industries, not only to overcome but to excel in this situation. In the present era, cut-throat competition, fluctuating demands, customization of product, very high initial investment and ever increasing manpower cost, are severely affecting the profit margins of manufacturing industry. A refinement in the results is observed with adoption of principal component analysis and Taguchi’s method. Numerical example is explained to illustrate the approach. Further, the proposed heuristic is modified for the application of principal component analysis and Taguchi’s method. The heuristic minimizes inter-cellular movement cost/time.
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. It leads to the invention and implementation of highly advanced and complex cell formation methods. The consideration to this realistic data makes cell formation problem very complex and tedious. In cellular manufacturing, consideration to manufacturing flexibility and production-related data is vital for cell formation. Appropriate manufacturing cells formation is the first step in designing a cellular manufacturing system. 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.
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