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Design and Analysis of Simulation Experiments  
Design and Analysis of Simulation Experiments
von: Jack P. C. Kleijnen
Springer-Verlag, 2007
ISBN: 9780387718132
220 Seiten, Download: 4159 KB
 
Format:  PDF
geeignet für: Apple iPad, Android Tablet PC's Online-Lesen PC, MAC, Laptop

Typ: B (paralleler Zugriff)

 

 
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Inhaltsverzeichnis

  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  


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