Neural network metamodeling for cycle time-throughput profiles in manufacturing
AbstractThis paper proposed a neural network (NN) metamodeling method to generate the cycle time (CT)-throughput (TH) profiles for single/multi-product manufacturing environments. Such CT-TH 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 CT-TH 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.
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Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 205 (2010)
Issue (Month): 1 (August)
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Web page: http://www.elsevier.com/locate/eor
Discrete event simulation Response surface modeling Design of experiments Neural networks Semiconductor manufacturing Queueing;
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