A fuzzy genetic algorithm for high-tech cellular manufacturing system design

With the natural characteristic of multi-process planning, a flexible manufacturing system (FMS) is much more productive than the conventional production approaches because its production processes are flexible so that the bottleneck activities in the production system can be significantly reduced. Therefore, it becomes a high efficient production system due to shorten time of manufacturing lead time, low volume of work-in-process, better performance of line-balancing and high utilization of machinery. The purpose of this research is to propose a design philosophy of part/machine grouping and cellular manufacturing system formulating with the involvement of multi-process-plan based on the techniques of fuzzy theory and genetic algorithm. The multiple objectives of this approach include minimizing the number of movements between the cells, maximizing the production density within the cells and minimizing the production loading variance within the cells. With the needs mentioned above, this research develops a fuzzy genetic clustering analysis model, which integrates fuzzy theory and genetic algorithm. This generalized clustering analysis model uses the input data of machine/part matrix with multi-process-plan, fuzzy production demands and fuzzy technical feasibility of machines under the conditions that the movements between the cells should be minimized, the production density within the cells should be maximized and the production loading variance should be minimized. This proposed prototyped model fuzzifies some essential manufacturing factors and attempts to explore a better result for cellular manufacturing system design in order to make the model with much applicability for high technology industries in the real world.