Cell formation with ordinal-level data using ART1-based neural networks

Abstract

In Cell Formation Problem (CFP), the zero-one Part-Machine Incidence Matrix (PMIM) is the common input to any clustering algorithm. The output is generated with two or more machine cells and corresponding part families. The major demerit with such models is that real-life production factors such as operation time, sequence of operations and lot size of the product are not accounted for. In this paper, the operation sequence of the parts is considered to enhance the quality of the solution. A neural network-based algorithm is proposed to solve the CFP. The performance of the proposed algorithm is tested with example problems and the results are compared with the existing methods found in the literature. The results presented clearly shows that the performance of the proposed ART1-based algorithm is comparable with the other methods.