Application of visual clustering properties of self organizing map in machine–part cell formation

摘要: Cellular manufacturing (CM) is an approach that includes both flexibility of job shops and high production rate flow lines. Although CM provides many benefits in reducing throughput times, setup work-in-process inventories but the design complex NP complete problem. The cell formation problem based on operation sequence (ordinal data) rarely reported literature. objective present paper to propose a visual clustering for machine-part using self organizing map (SOM) algorithm unsupervised neural network achieve better group technology efficiency measure as well SOM quality. work also has established criteria choosing optimum size results quantization error, topography average distortion during training which have generated best preservation topology. To evaluate performance proposed algorithm, we tested several benchmark problems available show not only generates accurate solution any reported, so far, literature also, some instances produced are even than previously results. effectiveness statistically verified.