Abstract
Cellular manufacturing system (CMS) is an important application in group technology. CMS design involves identifying part families and machine groups. Genetic algorithm (GA) is a robust adaptive optimization method based on principles of natural evolution, having the advantages of parallel processing and multi-points searching. GA search method is well suited for the machine-component grouping (MCG) problem. In this work, we present a mathematical programming formulation which considers the intercell part flow and the manufacturing cell density for the MCG problem. A two-phased algorithm based on GA is also proposed to solve the problem. The only assumption for the proposed algorithm to be known in advance is the upper bound of total number of manufacturing cells. The proposed algorithm can provide a feasible cell grouping under machine capacity constraints. Moreover, performance trials suggest that the proposed algorithm can provide a robust solution to the MCG problem in a short execution time.