Titlebook: Design and Analysis of Simulation Experiments; Jack P.C. Kleijnen Book 2015Latest edition Springer International Publishing Switzerland 20

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Kriging Metamodels and Their Designs,ing models through . (FANOVA) using Sobol’s indexes. Section 5.9 discusses . (RA) or . (UA). Section 5.10 discusses several remaining issues. Section 5.11 summarizes the major conclusions of this chapter, and suggests topics for future research. The chapter ends with Solutions of exercises, and a lo

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Simulation Optimization,imated optimum is truly optimal—using the . (KKT) conditions. Section 6.3 discusses the use of Kriging metamodels for optimization. Section 6.3.1 presents . (EGO), which uses Kriging. Section 6.3.2 presents . (KrIMP) for the solution of problems with constrained outputs. Section 6.4 discusses . (RO)

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Book 2015Latest editiontogether, this new edition has approximately 50% new material not in the original book.  More specifically, the author has made significant changes to the book’s organization, including placing the chapter on Screening Designs immediately after the chapters on Classic Designs, and reversing the orde

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Classic Regression Metamodels and Their Designs,iscusses black-box versus white-box approaches in the . (DASE). Section 2.2 covers the basics of linear regression analysis. Section 2.3 focuses on first-order polynomial regression. Section 2.4 presents designs for estimating such first-order polynomials; namely, so-called . (R-III) designs. Sectio

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Classic Assumptions Versus Simulation Practice,. Section 3.2 discusses multiple simulation outputs (responses, performance measures), which are usual in simulation practice. Section 3.3 addresses possible nonnormality of either the simulation output itself or the regression residuals (fitting errors), including tests of normality, normalizing tr

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Screening the Many Inputs of Realistic Simulation Models,lation models that have “very many” inputs (say, hundreds of inputs); this section also gives an overview of several screening methods. Section 4.2 explains a screening method called . (SB); for simplicity, this section assumes deterministic simulation and first-order polynomial metamodels. Section 

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Kriging Metamodels and Their Designs,ion 5.2 details so-called . (OK), including the basic Kriging assumptions and formulas assuming deterministic simulation. Section 5.3 discusses parametric bootstrapping and conditional simulation for estimating the variance of the OK predictor. Section 5.4 discusses . (UK) in deterministic simulatio

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Simulation Optimization,simulation; this optimization we call . or briefly .. There are many methods for this optimization, but we focus on methods that use specific metamodels of the underlying simulation models; these metamodels were detailed in the preceding chapters, and use either linear regression or Kriging. Section

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Amber Dudley,Roumyana Slabakovaiscusses black-box versus white-box approaches in the . (DASE). Section 2.2 covers the basics of linear regression analysis. Section 2.3 focuses on first-order polynomial regression. Section 2.4 presents designs for estimating such first-order polynomials; namely, so-called . (R-III) designs. Sectio