Machine Cell Formation with Alternative Routings based on Genetic Algorithm

Abstract

In automated batch type production systems, machine-part cell formation problems (CFP) have long drawn attention of researchers. The objective of CFP in cellular manufacturing system (CMS) is to identity machine cells and part families in order to minimize the intercellular movements of parts as well as maximize the utilization of machines. Optimum cell formation results reduction in total production times, in-process inventories, material handling cost, labor cost/times, paper works, number of machine set-ups, set-up times. It also simplifies process plans, management and improves product quality, productivity, utilization of resources. Since the modern manufacturing machines are generally multifunctional, so the processing of parts can be performed by number of alternative routes. The objectives of this study are to determine the optimal machine cells with optimal processing route and balanced machine cells (minimum cell load variation). Here a genetic algorithm heuristic approach is presented for six benchmark problems with multiple process routes, sequence of processes and parts volume. Computational outcome show that the proposed heuristic gives better result comparatively with the well-known existing methods in terms of total intercellular movements.