What is included with this book?
Introduction | p. 1 |
What is simulation? | p. 1 |
What is DASE? | p. 7 |
DASE symbols and terms | p. 10 |
Solutions for exercises | p. 12 |
Low-order polynomial regression metamodels and their designs: basics | p. 15 |
Introduction | p. 16 |
Linear regression analysis: basics | p. 19 |
Linear regression analysis: first-order polynomials | p. 27 |
First-order polynomial with a single factor | p. 27 |
First-order polynomial with several factors | p. 28 |
Designs for first-order polynomials: resolution-III | p. 36 |
2[superscript k-p] designs of resolution-III | p. 36 |
Plackett-Burman designs of resolution-III | p. 39 |
Regression analysis: factor interactions | p. 40 |
Designs allowing two-factor interactions: resolution-IV | p. 42 |
Designs for two-factor interactions: resolution-V | p. 46 |
Regression analysis: second-order polynomials | p. 49 |
Designs for second-degree polynomials: Central Composite Designs (CCDs) | p. 50 |
Optimal designs and other designs | p. 51 |
Validation of metamodels | p. 54 |
Coefficients of determination and correlation coefficients | p. 54 |
Cross-validation | p. 57 |
More simulation applications | p. 63 |
Conclusions | p. 66 |
Appendix: coding of nominal factors | p. 66 |
Solutions for exercises | p. 69 |
Classic assumptions revisited | p. 73 |
Introduction | p. 73 |
Multivariate simulation output | p. 74 |
Designs for multivariate simulation output | p. 77 |
Nonnormal simulation output | p. 78 |
Realistic normality assumption? | p. 78 |
Testing the normality assumption | p. 79 |
Transformations of simulation I/O data, jackknifing, and bootstrapping | p. 80 |
Heterogeneous simulation output variances | p. 87 |
Realistic constant variance assumption? | p. 87 |
Testing for constant variances | p. 88 |
Variance stabilizing transformations | p. 89 |
LS estimators in case of heterogeneous variances | p. 89 |
Designs in case of heterogeneous variances | p. 92 |
Common random numbers (CRN) | p. 93 |
Realistic CRN assumption? | p. 94 |
Alternative analysis methods | p. 94 |
Designs in case of CRN | p. 96 |
Nonvalid low-order polynomial metamodel | p. 97 |
Testing the validity of the metamodel | p. 97 |
Transformations of independent and dependent regression variables | p. 98 |
Adding high-order terms to a low-order polynomial metamodel | p. 98 |
Nonlinear metamodels | p. 99 |
Conclusions | p. 99 |
Solutions for exercises | p. 100 |
Simulation optimization | p. 101 |
Introduction | p. 101 |
RSM: classic variant | p. 105 |
Generalized RSM: multiple outputs and constraints | p. 110 |
Testing an estimated optimum: KKT conditions | p. 116 |
Risk analysis | p. 123 |
Latin Hypercube Sampling (LHS) | p. 126 |
Robust optimization: Taguchian approach | p. 130 |
Case study: Ericsson's supply chain | p. 135 |
Conclusions | p. 137 |
Solutions for exercises | p. 138 |
Kriging metamodels | p. 139 |
Introduction | p. 139 |
Kriging basics | p. 140 |
Kriging: new results | p. 147 |
Designs for Kriging | p. 149 |
Predictor variance in random simulation | p. 151 |
Predictor variance in deterministic simulation | p. 152 |
Related designs | p. 154 |
Conclusions | p. 155 |
Solutions for exercises | p. 156 |
Screening designs | p. 157 |
Introduction | p. 157 |
Sequential Bifurcation | p. 160 |
Outline of simplest SB | p. 160 |
Mathematical details of simplest SB | p. 165 |
Case study: Ericsson's supply chain | p. 167 |
SB with two-factor interactions | p. 169 |
Conclusions | p. 171 |
Solutions for exercises | p. 172 |
Epilogue | p. 173 |
References | p. 175 |
Index | p. 211 |
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