Preface	7
	Contents	10
	Introduction	13
	What is simulation?	13
	What is DASE?	19
	DASE symbols and terms	22
	Solutions for exercises	24
	Low-order polynomial regression metamodels and their designs: basics	26
	Introduction	27
	Linear regression analysis: basics	30
	Linear regression analysis: first-order polynomials	38
	First-order polynomial with a single factor	38
	First-order polynomial with several factors	39
	Designs for first-order polynomials: resolution-III	47
	2k-p designs of resolution-III	47
	Plackett-Burman designs of resolution-III	50
	Regression analysis: factor interactions	51
	Designs allowing two-factor interactions: resolution-IV	53
	Designs for two-factor interactions: resolution-V	57
	Regression analysis: second-order polynomials	60
	Designs for second-degree polynomials: Central Composite Designs (CCDs)	61
	Optimal designs and other designs	62
	Validation of metamodels	65
	Coefficients of determination and correlation coefficients	65
	Cross-validation	68
	More simulation applications	74
	Conclusions	77
	Appendix: coding of nominal factors	77
	Solutions for exercises	80
	Classic assumptions revisited	83
	Introduction	83
	Multivariate simulation output	84
	Designs for multivariate simulation output	87
	Nonnormal simulation output	88
	Realistic normality assumption?	88
	Testing the normality assumption	89
	Transformations of simulation I/O data, jackknifing, and bootstrapping	90
	Heterogeneous simulation output variances	97
	Realistic constant variance assumption?	97
	Testing for constant variances	98
	Variance stabilizing transformations	99
	LS estimators in case of heterogeneous variances	99
	Designs in case of heterogeneous variances	102
	Common random numbers (CRN)	103
	Realistic CRN assumption?	104
	Alternative analysis methods	104
	Designs in case of CRN	106
	Nonvalid low-order polynomial metamodel	107
	Testing the validity of the metamodel	107
	Transformations of independent and dependent regression variables	108
	Adding high-order terms to a low-order polynomial metamodel	108
	Nonlinear metamodels	109
	Conclusions	109
	Solutions for exercises	110
	Simulation optimization	111
	Introduction	111
	RSM: classic variant	115
	Generalized RSM: multiple outputs and constraints	120
	Testing an estimated optimum: KKT conditions	126
	Risk analysis	133
	Latin Hypercube Sampling (LHS)	136
	Robust optimization: Taguchian approach	140
	Case study: Ericsson's supply chain	145
	Conclusions	147
	Solutions for exercises	148
	Kriging metamodels	149
	Introduction	149
	Kriging basics	150
	Kriging: new results	157
	Designs for Kriging	159
	Predictor variance in random simulation	161
	Predictor variance in deterministic simulation	162
	Related designs	164
	Conclusions	165
	Solutions for exercises	166
	Screening designs	167
	Introduction	167
	Sequential Bifurcation	170
	Outline of simplest SB	170
	Mathematical details of simplest SB	175
	Case study: Ericsson's supply chain	177
	SB with two-factor interactions	179
	Conclusions	181
	Solutions for exercises	182
	Epilogue	183
	References	185
	Index	221