@techreport{c7f60f029dc541fc897f2e58c1824f01,
title = "Simulation Optimization through Regression or Kriging Metamodels",
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.",
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)",
author = "J.P.C. Kleijnen",
year = "2017",
month = may,
day = "16",
language = "English",
volume = "2017-026",
series = "CentER Discussion Paper",
publisher = "CentER, Center for Economic Research",
type = "WorkingPaper",
institution = "CentER, Center for Economic Research",
}