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Application of genetic algorithm in generalized machine cell formation problem

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 is to determine the optimal processing route and balanced machine cells (to minimize cell load variation) incorporation with parts volume, process sequence for minimum intercellular movements of parts. A genetic algorithm metaheuristic approach is presented for a benchmark CFP. Computational results show that the proposed approach gives better result comparatively with the well-known existing methods in terms of total intercellular movements.