Generalized machine cell formation considering material flow and plant layout using modified self-organizing feature maps

In this paper, we develop a new generalized machine cell formation technique considering production sequences, lot sizes, processing times, of machine grouping, and the plant layout via the self-organizing feature maps (SOFM). In particular, second layer node architecture is construct to reflect the above factors. From the result of SOFM learning, a generalized machine cell formation matrix is obtained such that the material flow is reduced and the spacial restriction for the layout is reasonably considered. The adopted performance measures include the recovery ratio of bond energy, the grouping efficacy, the total inter-cell movement, and the computational time. The results are compared with the ranl order clustering method (ROC), the direct clustering analysis method (DCA), and the spark clustering analysis. The experimental result shows that the proposed method performs well in the sense of material flow reduction and layout efficiency, as well as in the sense of robustness and some other functionalities.