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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 205 (2010)
Issue (Month): 1 (August)
Contact details of provider:
Web page: http://www.elsevier.com/locate/eor
Discrete event simulation Response surface modeling Design of experiments Neural networks Semiconductor manufacturing Queueing;
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.