Fractional cell formation in group technology using modified ART1 neural networks

Author(s): Haq, A.N. | Venkumar, P. |

Year: 2006

Citation: Volume 28, Numbers 7-8

Abstract: Group technology (GT) is a manufacturing philosophy that attempts to reduce production cost by reducing the material handling and transportation cost. The GT cell formation by any known algorithm/heuristics results in much intercell movement known as exceptional elements. In such cases, fractional cell formation using reminder cells can be adopted successfully to minimize the number of exceptional elements. The fractional cell formation problem is solved using modified adaptive resonance theory1 network (ART1). The input to the modified ART1 is machine-part incidence matrix comprising of the binary digits 0 and 1. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or superior to other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.

Topics: Machine Learning, Applications: Industrial Control, Models: ART 1,

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