Neural network metamodeling for cycle time-throughput profiles in manufacturing
This paper proposed a neural network (NN) metamodeling method to generate the cycle time (CT)throughput (TH) profiles for single/multi-product manufacturing environments. Such CTTH profiles illustrate the trade-off relationship between CT and TH, the two critical performance measures, and hence provide a comprehensive performance evaluation of a manufacturing system. The proposed methods distinct from the existing NN metamodeling work in three major aspects: First, instead of treating an NN as a black box, the geometry of NN is examined and utilized; second, a progressive model-fitting strategy is developed to obtain the simplest-structured NN that is adequate to capture the CTTH relationship; third, an experiment design method, particularly suitable to NN modeling, is developed to sequentially collect simulation data for the efficient estimation of the NN models.
neural networks discrete event simulation response surface modeling design of experiments semiconductor manufacturing queueing