Utilization-based grouping efficiency and multi-criteria decision approach in designing of manufacturing cells

The essence of the “ratio data” in cellular manufacturing system has rarely been correctly emphasized since past few decades of research and study. This article is an attempt to deal with cell formation problem which exploits machine utilization percentage and eliminates the “fuzziness” on the subject of ratio data. In the course of this study, a distinctive technique is adopted based on multi-criteria decision technique with significant modifications which is experimented on test data and compared with published methodology. To verify the grouping result, a novel performance measure is proposed and elaborated analytically to establish its superiority and robustness over the earlier efficiency measures published in the past literature of cellular manufacturing system. The novelty of this research remains in eradicating ambiguities on the subject of “ratio data,” imposing appropriate rules while generating the test problems, catering a unique multi-criteria decision technique–based approach and a novel performance measure for the aforesaid problem.

A Neural Network-Based Algorithm for Group Technology

In this paper, we propose a neural network-based algorithm for grouping machine and parts in cellular manufacturing. For grouping machines, we develop similarity coefficients which take into account both similarity and dissimilarity between machines. The machine cells are formed by an algorithm which is based on the maximum neural network. Another algorithm is used to find the part families associated with each machine cell. When compared with an existing algorithm, our algorithm shows better performance in terms of grouping efficiency and grouping efficacy.