A general search algorithm for cell formation in group technology

Abstract

Group Technology (GT) is a manufacturing approach, which organizes and uses the information about an item's similarity (parts and/or machines) to enhance efficiency and effectiveness of batch manufacturing systems. The application of group technology to manufacturing requires the identification of part families and formation of associated machine-cells. One approach is the Similarity Coefficient Method (SCM), an effective clustering technique for forming machine cells. SCM involves a hierarchical machine grouping process in accordance with computed ‘similarity coefficients’. While SCM is capable of incorporating manufacturing data into the machine-part grouping process, it is very sensitive to the data to be clustered (Chan and Milner 1982). It has been argued that for SCM to be meaningful, all machines must process approximately the same numbers of parts (Chan and Milner 1982).We present a new approach, based on artificial intelligence principles, to overcome some of these problems by incorporating an evaluation function into the grouping process. Our goal is to provide a method that is both practical and flexible in its use for the process of cell formation. Our method uses the similarity matrix to generate the feasible machine groups. Then an evaluation function is applied to select a machine-cell arrangement through an iterative process. The approach features a graph-based representation (N-tuple) to represent the problem and illustrate the solution strategies. Also, we develop an algorithm to search for the most promising machine groups from the graph. Compared with Single Linkage Clustering and Average Linkage Clustering approaches, our approach attains comparable or better results