Design and Analysis of Simulation Experiments

Transcription

1 Jack P.C. Kleijnen Design and Analysis of Simulation Experiments Second Edition ~Springer

2 Contents Preface vii 1 Introduction What Is Simulation? What Is "Design and Analysis of Simulation Experiments" (DASE)? DASE Symbols and Terminology 12 Solutions for Exercises 16 References Classic Regression Metamodels and Their Designs Introduction Linear Regression Basic Linear Regression Analysis Advanced Linear Regression Analysis Linear Regression: First-Order Polynomials Scaling the Inputs One-Factor-at-a-Time Designs Versus Factorial Designs Designs for First-Order Polynomials: Resolution-III k-p Designs of Resolution-III Plackett-Burman Designs of Resolution-III. 54 xi

3 xii Contents 2.5 Linear Regression: Interactions Designs Allowing Two-Factor Interactions: Resolution-IV Designs for Two-Factor Interactions: Resolution-V Linear Regression: Second-Order Polynomials Designs for Second-Degree Polynomials: Central Composite Designs Optimal Designsand Other Designs Optimal Designs More Design Types Conclusions Solutions for Exercises 73 References Classic Assumptions Versus Simulation Practice Introduction Multivariate Output Linear Regression Metamodels Designs for Multivariate Simulation Output Nonnormal Output Realistic Normality Assumption? Testing the Normality Assumption Normalizing Transformations Jackknifing Bootstrapping Heterogeneous Output Variances Realistic Constant Variance Assumption? Testing for Constant Variances Variance Stabilizing Transformations Least Squares Estimators Designs for Heterogeneous Output Variances Common Random Numbers (CRN) Validation of Metamodels The Coefficients of Determination R 2 and R!ij Cross-Validation Transformations of Regression Variables Adding High-Order Terms Conclusions Solutions for Exercises 123 References Screening the Many Inputs of Realistic Simulation Models Introduction Sequential Bifurcation (SB) for Deterministic Simulations and First-Order Polynomial Metamodels. 139

4 Contents xiii 4.3 SB for Deterministic Simulations and Second-Order Polynomial Metamodels SB for Random Simulations and Constant Number of Replications The SB Method Case Study: Ericsson's Supply Cbain SB for Random Simulations and Variable Number of Replications Monte Carlo Experiment with SPRT Multiresponse SB: MSB Monte Carlo Experiments with MSB and SB Case Study: Chinese Supply-Chain Validating the SB and MSB Assumptions Conclusions Solutions for Exercises 173 References Kriging Metamodels and Their D esigns Introduction Ordinary Kriging (OK) in Deterministic Simulation OK Basics Estimating the OK Parameters Bootstrapping and Conditional Simulation for OK in Deterministic Simulation Bootstrapped OK (BOK) Conditional Simulation of OK (CSOK) Universal Kriging (UK) in Deterministic Simulation Designs for Deterministic Simulation Latin Hypercube Sampling (LHS) Sequential Customized Designs Stochastic Kriging (SK) in Random Simulation A Metamodel for SK Designs for SK Monotonie Kriging: Bootstrapping and Acceptance/ Rejection Global Sensitivity Analysis: Sobol's FANOVA Risk Analysis Miscellaneous Issues in Kriging Conclusions Solutions for Exercises 224 References

5 xiv Contents 6 Simulation Optimization 6.1 Introduction Linear Regression for Optimization Response Surface Methodology (RSM): Basics RSM in Random Simulation Adapted Steepest Descent (ASD) for RSM Multiple Responses: Generalized RSM (GRSM) Testing a GRSM Optimum: Karush-Kuhn-Tucker (KKT) conditions Kriging Metamodels for Optimization Effi.cient Global Optimization (EGO) Kriging and Integer Mathematical Programming (KrIMP) Robust Optimization Taguchian Robust Optimization Through RSM Taguchian Robust Optimization Through Kriging Ben-Tal et al. 's Robust Optimization. 6.5 Conclusions... Solutions for Exercises References.... Author Index Subject Index