CASE: A clustering algorithm for cell formation with sequence data

Cellular manufacturing is a well-known strategy for reducing lead times in batch production systems. Most of the methods of cell formation are based on machine-component incidence alone. However, other factors such as production sequence and product volumes, if incorporated, can enhance the quality of the solutions. This study uses sequence data for cell-formation. A new similarity measure is defined for this purpose and appropriate measures of performance for evaluating solutions are introduced. A clustering approach of the non-hierarchical type is proposed. With new seeding techniques, the proposed algorithm clusters machines and components on the basis of sequence data. The algorithm gives encouraging results when applied to sample problems.