Thursday, July 23, 2015

Design and Analysis of Simulation Experiments






Design and Analysis of Simulation Experiments
Buku ini diterbitkan tahun 2008  oleh  Springer Science+Business Media, LLC adalah buku edisi  Pertama.


Judul:  Design and Analysis of Simulation Experiments
Oleh:   Jack P.C. Kleijnen
Penerbit:  Springer Science+Business Media, LLC
Tahun: 2008
Jumlah Halaman: 229 hal.


Penulis:
Jack P.C. Kleijnen

Tilburg University
Tilburg, The Netherlands

Lingkup Pembahasan:
Tiga bab pertama dalam buku ini  menggambarkan bab berikutnya,  meskipun bab-bab tersebut bersifat independen satu sama lain, sehingga dimungkinkan dibaca dalam urutan yang paling sesuai dengan kepentingan pembaca individu.
Pembahasan buku ini mencakup  Dasar-dasar metamodels regresi polinomial dan desain Orde rendah,   Revisi Asumsi klasik, Optimasi simulasi, Metamodels kriging, dan Desain skrining.


Daftar Isi:

 

1  Introduction 1
    1.1     What is simulation?  1
    1.2     What is DASE?  7
    1.3     DASE symbols and terms  10
    1.4     Solutions for exercises  12
2  Low-order polynomial regression metamodels and their designs: basics 15
    2.1     Introduction  16
    2.2     Linear regression analysis: basics  19
    2.3     Linear regression analysis: first-order polynomials   27
        2.3.1 First-order polynomial with a single factor  27
        2.3.2 First-order polynomial with several factors   28
    2.4     Designs for first-order polynomials: resolution-III    36
        2.4.1 2k−p designs of resolution-III   36
        2.4.2 Plackett-Burman designs of resolution-III  39
    2.5     Regression analysis: factor interactions   40
    2.6     Designs allowing two-factor interactions: resolution-IV    42
    2.7     Designs for two-factor interactions: resolution-V   46
    2.8     Regression analysis: second-order polynomials   49
    2.9     Designs for second-degree polynomials: Central Composite Designs (CCDs)    50
    2.10   Optimal designs and other designs  51
    2.11   Validation of metamodels  54
        2.11.1 Coefficients of determination and correlation coefficients    54
        2.11.2 Cross-validation   57
    2.12   More simulation applications  63
    2.13   Conclusions  66
    2.14   Appendix: coding of nominal factors  66
    2.15   Solutions for exercises   69
3 Classic assumptions revisited 73
    3.1     Introduction   73
    3.2     Multivariate simulation output  74
        3.2.1 Designs for multivariate simulation output   77
    3.3     Nonnormal simulation output   78
        3.3.1 Realistic normality assumption?  78
        3.3.2 Testing the normality assumption  79
        3.3.3 Transformations of simulation I/O data, jackknifing, and bootstrapping   80
    3.4     Heterogeneous simulation output variances  87
        3.4.1 Realistic constant variance assumption?  87
        3.4.2 Testing for constant variances  88
        3.4.3 Variance stabilizing transformations 89
        3.4.4 LS estimators in case of heterogeneous variances   89
        3.4.5 Designs in case of heterogeneous variances  92
    3.5     Common random numbers (CRN)  93
        3.5.1 Realistic CRN assumption? 94
        3.5.2 Alternative analysis methods 94
        3.5.3 Designs in case of CRN   96
    3.6     Nonvalid low-order polyno mial metamodel 97
        3.6.1 Testing the validity of the metamodel  97
        3.6.2 Transformations of independent and dependent regression variables  98
        3.6.3 Adding high-order terms to a low-order polynomial metamodel  98
        3.6.4 Nonlinear metamodels  99
    3.7     Conclusions  99
    3.8     Solutions for exercises  100
4  Simulation optimization 101
    4.1     Introduction  101
    4.2     RSM: classic variant  105
    4.3     Generalized RSM: multiple outputs and constraints  110
    4.4     Testing an estimated optimum: KKT conditions  116
    4.5     Risk analysis  123
        4.5.1 Latin Hypercube Sampling (LHS)  126
    4.6     Robust optimization: Taguchian approach  130
        4.6.1 Case study: Ericsson’s supply chain  135
    4.7     Conclusions  137
    4.8     Solutions for exercises   138
5  Kriging metamodels 139
    5.1     Introduction  139
    5.2     Kriging basics  140
    5.3     Kriging: new results  147
    5.4     Designs for Kriging   149
        5.4.1 Predictor variance in random simulation  151
        5.4.2 Predictor variance in deterministic simulation 152
        5.4.3 Related designs   154
    5.5     Conclusions  155
    5.6     Solutions for exercises  156
6  Screening designs 157
    6.1     Introduction  157
    6.2     Sequential Bifurcation  160
        6.2.1 Outline of simplest SB   160
        6.2.2 Mathematical details of simplest SB  165
        6.2.3 Case study: Ericsson’s supply chain  167
        6.2.4 SB with two-factor interactions   169
    6.3     Conclusions   171
    6.4     Solutions for exercises   172
7  Epilogue 173

References 175
Index 211

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