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Design and Analysis of Simulation Experiments

  • Jack P. C. Kleijnen
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 231)

Abstract

This contribution summarizes the design and analysis of experiments with computerized simulation models. It focuses on two metamodel (surrogate, emulator) types, namely first-order or second-order polynomial regression, and Kriging (or Gaussian process). The metamodel type determines the design of the simulation experiment, which determines the input combinations of the simulation model. Before applying these metamodels, the analysts should screen the many inputs of a realistic simulation model; this contribution focuses on sequential bifurcation. Optimization of the simulated system may use either a sequence of first-order and second-order polynomials—so-called response surface methodology (RSM)—or Kriging models fitted through sequential designs—including efficient global optimization (EGO). Robust optimization accounts for uncertainty in some simulation inputs.

Keywords

Robustness and sensitivity Metamodel Design Regression Kriging 

Notes

Acknowledgements

I thank the editors for inviting me to write a contribution for this book and W. Shi (Hubei University of Economics, Wuhan, China) for commenting on Sect. 1.4.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Tilburg UniversityTilburgNetherlands

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