MACHINE-PART FAMILY FORMATION WITH THE ADAPTIVE RESONANCE THEORY PARADIGM

The ART1 neural network paradigm employs a heuristic where new vectors are compared with group representative vectors for classification. ART1 is adapted for the cell formation problem by reordering input vectors and by using a better representative vector. This is validated with both test cases studied in literaure as well as synthetic matrices. Algorithmns for effective use of ART1 are proposed. This approach is observed to produce sufficiently accurate results and is therefore promising in both speed and functionality. For the automatic generation of an optimal family formation solution a decision support system can be integrated with ART1.