Cellular Formation Based on Evolutionary Optimization of Granules

Cellular formation is a key issue of group technology for lot size production. The purpose of cellular formation is to generate the similar machines into a cell that are applied for producing the similar parts. In this research, this problem is simply considered as the clustering analysis for a two-dimensional data, an incidence matrix of part and machine. The problem itself is inherently association with machine granules and machine granulation. Simply speaking, one may think it is to produce an appropriate number of granules that cover almost all of the data. To construct a collection of boxes (machine granules), two limitations should be satisfied: (1) they cover as many data points as possible so that the density of the data within the boxes is made as high as possible, (2) the boxes are as compact as possible so that we cover the data but do not stretch the boxes two excessively into the poorly populated data space. Genetic algorithm (GA), a search method utilizing the principles of natural selection and genetics, is one of the most popular methods of evolutionary computation. Therefore, GA can be applied for searching a collection of boxes under the constraint of these two limitations. The advantages of this technique are the simplicity of computation, the capability of handling a large scale of clustering analysis, and the reduction of the possibility of falling local minimum. In this research, we attempt to solve the cellular formation problem using the integration of granule computation and genetic algorithm. The contributions include the encoding of chromosome-like solutions, the definition of fitness function.