Quantized Hopfield networks and tabu search for manufacturing cell formation problems

The use of neural networks in the design of cellular manufacturing system is not new. This paper presents an application of modified Hopfield neural networks in order to solve cell formation problems: the quantized and fluctuated Hopfield neural networks (QFHN). This kind of Hopfield network combined with the "tabu search" approach were primarily used in a hybrid procedure in order to solve the cell formation for big sizes industrial data set. The problem is formulated as a 0/1 linear and integer programming model in order to minimize the dissimilarities between machines and/or parts. Our hybrid approach allows us to obtain optimal or nearly optimal solutions very frequently and much more quickly than traditional Hopfield networks. It is also illustrated that the fluctuation associated with this quantization may enable the network to escape from local minima, to converge to global minima, and consequently to obtain optimal solutions very frequently and much more quickly than pure quantized Hopfield networks (QHN). The effectiveness of the proposed approach is flexibility it gives us, for example, in time problem-solving for large-scale and speed of execution when we apply it.