Taxonomy of Production Systems with Combining K-Means and Evolutionary Algorithms

Abstract

The study of production systems taxonomy not only provides a good description of organization dominant groups but also provides the ground for more specialized studies such as a study of performance, the proper form of production decisions in each group, and the theorizing in it. In some taxonomic studies, due to high speed and ease of implementation, K-means cluster analysis was used to analyze data but the convergence took place in local optimum. For this reason, Hybrid Clustering Algorithms were used for the clustering of manufacturing companies. The clustering results of these methods were compared using validation indicators. According to the results of the comparisons, the best clustering algorithm was chosen, based on which cluster naming was done. Then, using the results of Discriminant Analysis, the distinctive dimensions of clusters were identified and the results showed that the manufacturing systems in Iran can be introduced in two dimensions of green production planning and resource capacity.