Simulation Optimization through Regression or Kriging Metamodels

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Abstract

This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These methods use either linear regression metamodels, or Kriging (Gaussian processes). The metamodel type guides the design of the experiment; this design …fixes the input combinations of the simulation model. These regression models uses a sequence of local fi…rst-order and second-order polynomials— known as response surface methodology (RSM). Kriging models are global, but are re-estimated through sequential designs. "Robust" optimization may use RSM or Kriging, and accounts for uncertainty in simulation inputs.
Original languageEnglish
Place of PublicationTilburg
PublisherCentER, Center for Economic Research
Number of pages21
Volume2017-026
Publication statusPublished - 16 May 2017

Publication series

NameCentER Discussion Paper
Volume2017-026

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Keywords

  • Cross-validation
  • Robust optimization
  • Regression analysis
  • Kriging
  • Guassian Process
  • response surface methodology (RSM)
  • efficient global optimization (EGO)
  • Taguchi
  • Boottrap
  • common rondom numbers (CRN)
  • Ltin hypercube sampling (LHS)
  • Karush-Kuhn-Tucker (KKT)

Cite this

Kleijnen, J. P. C. (2017). Simulation Optimization through Regression or Kriging Metamodels. (CentER Discussion Paper; Vol. 2017-026). Tilburg: CentER, Center for Economic Research.