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A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels

Author

Listed:
  • R D Hurrion

    (University of Warwick)

  • S Birgil

    (University of Warwick)

Abstract

This paper compares two forms of experimental design methods that may be used for the development of regression and neural network simulation metamodels. The experimental designs considered are full factorial designs and random designs. The paper shows that, for two example problems, neural network metamodels using a randomised experimental design produce more accurate and efficient metamodels than those produced by similar sized factorial designs with either regression or neural networks. The metamodelling techniques are compared by their ability to predict the results from two manufacturing systems that have different levels of complexity. The results of the comparison suggest that neural network metamodels outperform conventional regression metamodels, especially when data sets based on randomised simulation experimental designs are used to produce the metamodels rather than data sets from similar sized full factorial experimental designs.

Suggested Citation

  • R D Hurrion & S Birgil, 1999. "A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(10), pages 1018-1033, October.
  • Handle: RePEc:pal:jorsoc:v:50:y:1999:i:10:d:10.1057_palgrave.jors.2600812
    DOI: 10.1057/palgrave.jors.2600812
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    Citations

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    Cited by:

    1. Hachicha, Wafik & Ammeri, Ahmed & Masmoudi, Faouzi & Chachoub, Habib, 2010. "A comprehensive literature classification of simulation optimisation methods," MPRA Paper 27652, University Library of Munich, Germany.
    2. Batmaz, Inci & Tunali, Semra, 2003. "Small response surface designs for metamodel estimation," European Journal of Operational Research, Elsevier, vol. 145(2), pages 455-470, March.
    3. Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3516-3531, July.
    4. B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "Optimization of manufacturing systems using a neural network metamodel with a new training approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1191-1197, September.
    5. Mach Ɓukasz, 2017. "The Application of Classical and Neural Regression Models for the Valuation of Residential Real Estate," Folia Oeconomica Stetinensia, Sciendo, vol. 17(1), pages 44-56, June.

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