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Neural network metamodeling for cycle time-throughput profiles in manufacturing

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  • Yang, Feng

Abstract

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 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.

Suggested Citation

  • Yang, Feng, 2010. "Neural network metamodeling for cycle time-throughput profiles in manufacturing," European Journal of Operational Research, Elsevier, vol. 205(1), pages 172-185, August.
    • Handle: RePEc:eee:ejores:v:205:y:2010:i:1:p:172-185
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    3. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    4. Curry, B. & Morgan, P.H., 2006. "Model selection in Neural Networks: Some difficulties," European Journal of Operational Research, Elsevier, vol. 170(2), pages 567-577, April.
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    1. Batur, Demet & Bekki, Jennifer M. & Chen, Xi, 2018. "Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry," European Journal of Operational Research, Elsevier, vol. 264(1), pages 212-224.

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