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9780387718125

Design and Analysis of Simulation Experiments

by
  • ISBN13:

    9780387718125

  • ISBN10:

    0387718125

  • Format: Hardcover
  • Copyright: 2007-10-19
  • Publisher: Springer Verlag
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Summary

DASE: Design and Analysis of Simulation Experiments focuses on statistical methods for discrete-event simulation (such as queuing and inventory simulations). In addition, the book discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta) models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS). The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS. DASE: Design and Analysis of Simulation Experiments is an authoritative textbook and reference work for researchers, graduate students, and technical practitioners in simulation. Basic knowledge of simulation and mathematical statistics are expected; however, the book does summarize these basics, for the readers' convenience. In addition, the book provides relatively simple solutions for (a) selecting problems to simulate, (b) how to analyze the resulting data from simulation, and (c) computationally challenging simulation problems.

Author Biography

Jack Kleijnen is well known internationally for being a leading researcher in simulation for more than 30 years. He is the author of highly cited books in the area of statistical techniques in simulation that were published between 1974 and 1992. He is an excellent writer and researcher, and hence, ideally suited to write this important book for the field.  On 25 February 2008 Her Majesty Beatrix, Queen of the Netherlands, appointed Jack Kleijnen a Knight in the Order of the Netherlands Lion. 

Table of Contents

Introductionp. 1
What is simulation?p. 1
What is DASE?p. 7
DASE symbols and termsp. 10
Solutions for exercisesp. 12
Low-order polynomial regression metamodels and their designs: basicsp. 15
Introductionp. 16
Linear regression analysis: basicsp. 19
Linear regression analysis: first-order polynomialsp. 27
First-order polynomial with a single factorp. 27
First-order polynomial with several factorsp. 28
Designs for first-order polynomials: resolution-IIIp. 36
2[superscript k-p] designs of resolution-IIIp. 36
Plackett-Burman designs of resolution-IIIp. 39
Regression analysis: factor interactionsp. 40
Designs allowing two-factor interactions: resolution-IVp. 42
Designs for two-factor interactions: resolution-Vp. 46
Regression analysis: second-order polynomialsp. 49
Designs for second-degree polynomials: Central Composite Designs (CCDs)p. 50
Optimal designs and other designsp. 51
Validation of metamodelsp. 54
Coefficients of determination and correlation coefficientsp. 54
Cross-validationp. 57
More simulation applicationsp. 63
Conclusionsp. 66
Appendix: coding of nominal factorsp. 66
Solutions for exercisesp. 69
Classic assumptions revisitedp. 73
Introductionp. 73
Multivariate simulation outputp. 74
Designs for multivariate simulation outputp. 77
Nonnormal simulation outputp. 78
Realistic normality assumption?p. 78
Testing the normality assumptionp. 79
Transformations of simulation I/O data, jackknifing, and bootstrappingp. 80
Heterogeneous simulation output variancesp. 87
Realistic constant variance assumption?p. 87
Testing for constant variancesp. 88
Variance stabilizing transformationsp. 89
LS estimators in case of heterogeneous variancesp. 89
Designs in case of heterogeneous variancesp. 92
Common random numbers (CRN)p. 93
Realistic CRN assumption?p. 94
Alternative analysis methodsp. 94
Designs in case of CRNp. 96
Nonvalid low-order polynomial metamodelp. 97
Testing the validity of the metamodelp. 97
Transformations of independent and dependent regression variablesp. 98
Adding high-order terms to a low-order polynomial metamodelp. 98
Nonlinear metamodelsp. 99
Conclusionsp. 99
Solutions for exercisesp. 100
Simulation optimizationp. 101
Introductionp. 101
RSM: classic variantp. 105
Generalized RSM: multiple outputs and constraintsp. 110
Testing an estimated optimum: KKT conditionsp. 116
Risk analysisp. 123
Latin Hypercube Sampling (LHS)p. 126
Robust optimization: Taguchian approachp. 130
Case study: Ericsson's supply chainp. 135
Conclusionsp. 137
Solutions for exercisesp. 138
Kriging metamodelsp. 139
Introductionp. 139
Kriging basicsp. 140
Kriging: new resultsp. 147
Designs for Krigingp. 149
Predictor variance in random simulationp. 151
Predictor variance in deterministic simulationp. 152
Related designsp. 154
Conclusionsp. 155
Solutions for exercisesp. 156
Screening designsp. 157
Introductionp. 157
Sequential Bifurcationp. 160
Outline of simplest SBp. 160
Mathematical details of simplest SBp. 165
Case study: Ericsson's supply chainp. 167
SB with two-factor interactionsp. 169
Conclusionsp. 171
Solutions for exercisesp. 172
Epiloguep. 173
Referencesp. 175
Indexp. 211
Table of Contents provided by Ingram. All Rights Reserved.

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