Simulation

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Sprache:English
Veröffentlicht: Amsterdam [u.a.] Elsevier, NH 2006
Ausgabe:1. ed.
Schriftenreihe:Handbooks in operations research and management science 13
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adam_text Contents Dedication v CHAPTER 1 Stochastic Computer Simulation Shane G. Henderson and Barry L. Nelson 1 1 Scope of the Handbook 1 2 Key concepts in stochastic simulation 4 3 Organization of the Handbook 17 Acknowledgements ] 8 References 18 CHAPTER 2 Mathematics for Simulation Shane G. Henderson 19 1 Introduction 19 2 Static simulation: Activity networks 21 3 A model of ambulance operations 27 4 Finite horizon performance 28 5 Steady state simulation 34 Acknowledgements 50 Appendix: Proof of Proposition 15 50 References 52 CHAPTER 3 Uniform Random Number Generation Pierre LEcuyer 55 1 Introduction 55 2 Uniform random number generators 5( 3 Linear recurrences modulo in Ml 4 Generators based on recurrences modulo 2 M 5 Nonlinear RNGs 75 6 Empirical statistical tests 76 7 Conclusion, future work and open issues 77 Acknowledgements 78 References 78 vii viii Contents CHAPTER 4 Nonuniform Random Variate Generation Luc Devroye 83 1 The main paradigms 83 2 Uniformly bounded times 94 3 Universal generators 97 4 Indirect problems 99 5 Random processes 107 6 Markov chain methodology 108 Acknowledgement 116 References 116 CHAPTER 5 Multivariate Input Processes Bahar Biller and Soumyadip Ghosh 123 1 Introduction 123 2 Constructing full joint distributions 126 3 Parametric families of joint distributions 133 4 Constructing partially specified joint distributions 136 5 Conclusion 149 References 150 CHAPTER 6 Arrival Processes, Random Lifetimes and Random Objects Lawrence M. Leemis 155 1 Arrival processes 155 2 Generating random lifetimes 167 3 Generating random objects 172 Acknowledgements 178 References 178 CHAPTER 7 Implementing Representations of Uncertainty W. David Kelton 181 1 Introduction 181 2 Random number generation 182 3 Random structure generation 184 4 Application to variance reduction 186 5 Conclusions and suggestions 189 References 191 CHAPTER 8 Statistical Estimation in Computer Simulation Christos Alexopoulos 193 1 Introduction 193 2 Background 195 Contents ix 3 Sample averages and time averages 196 4 Stationary processes 199 5 Analyzing data from independent replications 204 6 Density estimation 210 7 Summary 220 Acknowledgements 222 References 222 CHAPTER 9 Subjective Probability and Bayesian Methodology Stephen E. Chick 225 Introduction 225 1 Main concepts 227 2 Computational issues 237 3 Input distribution and model selection 239 4 Joint input output models 240 5 Ranking and selection 245 6 Discussion and future directions 252 Acknowledgement 253 References 253 CHAPTER 10 A Hilbert Space Approach to Variance Reduction Roberto Szechtman 259 1 Introduction 259 2 Problem formulation and basic results 260 3 Hilbert spaces 263 4 A Hilbert space approach to control variates 27 i 5 Conditional Monte Carlo in Hilbert space 273 6 Control variates and conditional Monte Carlo from a Hilbert space perspective 274 7 Weighted Monte Carlo 275 8 Stratification techniques 278 9 Latin hypercube sampling 281 10 A numerical example 286 11 Conclusions 287 Acknowledgements 288 References 288 CHAPTER 11 Rare Event Simulation Techniques: An Introduction and Recent Advances S. Juneja and P. Shahabuddin 291 1 Introduction 291 2 Rare event simulation and importance sampling 296 3 Rare event simulation in a Markovian framework 302 4 Large deviations of multidimensional random walks 309 x Contents 5 Adaptive importance sampling techniques 316 6 Queueing systems 327 7 Heavy tailed simulations 330 8 Financial engineering applications 335 Acknowledgement 346 References 346 CHAPTER 12 Quasi Random Number Techniques C. Lemieux 351 1 Introduction 351 2 An example 356 3 A key concept: Effective dimension 359 4 Constructing quasi random point sets 364 5 Recurrence based point sets 369 6 Randomization techniques and variance results 371 7 Combination with other variance reduction techniques 374 8 Future directions 375 Acknowledgements 376 References 376 CHAPTER 13 Analysis for Design Ward Whitt 381 1 Introduction 381 2 The standard statistical framework 385 3 The asymptotic parameters for a function of a Markov chain 393 4 Birth and death examples 397 5 Diffusion processes 401 6 Stochastic process limits 405 7 Deleting an initial portion of the run to reduce bias 410 8 Directions for further research 411 Acknowledgement 411 References 411 CHAPTER 14 Resampling Methods R.C.H. Cheng 415 1 Introduction 415 2 The bootstrap 417 3 Quantiles and confidence intervals 422 4 Theory 428 5 Simulation models 436 6 Bootstrap comparisons 443 7 Bayesian models 446 Contents xi 8 Time series output 448 9 Final comments 451 References 451 CHAPTER 15 Correlation Based Methods for Output Analysis David Goldsman and Barry L. Nelson 455 1 Introduction 455 2 Motivation 457 3 Estimators using nonoverlapping batches 459 4 Estimators from overlapping batches 468 5 Summary and conclusions 473 Acknowledgement 474 References 474 CHAPTER 16 Simulation Algorithms for Regenerative Processes Peter W. Glynn 477 1 Introduction 477 2 The steady state simulation problem 478 3 The regenerative estimator for the TAVC 480 4 Choice of the optimal regeneration state 483 5 The regenerative approach to the initial transient and initial bias problems 484 6 When is a simulation regenerative? 487 7 When is a GSMP regenerative? 489 8 Algorithmic identification of regenerative structure 490 9 A martingale perspective on regeneration 493 10 Efficiency improvement via regeneration: Computing steady state gradients 496 11 Efficiency improvement via regeneration: Computing infinite horizon discounted reward 498 References 499 CHAPTER 17 Selecting the Best System Seong Hee Kim and Barry L. Nelson 501 1 Introduction 501 2 Basics of ranking and selection 502 3 Simulation issues and key results 508 4 Example procedures 517 5 Application 521 6 Asymptotic analysis 522 7 Other formulations 526 8 Future directions 53] Acknowledgements 532 References 532 xii Contents CHAPTER 18 Metamodel Based Simulation Optimization Russell R. Barton and Martin Meckesheimer 535 1 Introduction 535 2 Metamodels and simulation 538 3 Metamodel based optimization 545 4 Response surface methodology (RSM) 548 5 Global metamodel based optimization 563 6 Summary 569 Acknowledgements 570 References 570 CHAPTER 19 Gradient Estimation Michael C. Fu 575 1 Introduction 575 2 Gradient based simulation optimization 577 3 Indirect gradient estimation 580 4 Direct gradient estimation 583 5 Examples 598 6 Basic theoretical tools 605 7 Simple guidelines for the simulation practitioner 606 8 Applications 607 9 Probing further 609 10 Future research directions 611 Acknowledgements 612 References 612 CHAPTER 20 An Overview of Simulation Optimization via Random Search Sigrun Andradottir 617 1 Introduction 617 2 A brief review of random search methods 619 3 Convergence 621 4 Efficiency 624 5 Summary 629 Acknowledgements 630 References 630 CHAPTER 21 Metaheuristics Sigurdur Olafsson 633 1 Introduction 633 2 Background to metaheuristics 635 3 Accounting for simulation noise 640 Contents xiii 4 Genetic algorithm 643 5 Tabu search 644 6 The nested partition method 646 7 Making convergence statements 647 8 Future directions 652 References 653 Author Index 655 Subject Index 667
adam_txt Contents Dedication v CHAPTER 1 Stochastic Computer Simulation Shane G. Henderson and Barry L. Nelson 1 1 Scope of the Handbook 1 2 Key concepts in stochastic simulation 4 3 Organization of the Handbook 17 Acknowledgements ] 8 References 18 CHAPTER 2 Mathematics for Simulation Shane G. Henderson 19 1 Introduction 19 2 Static simulation: Activity networks 21 3 A model of ambulance operations 27 4 Finite horizon performance 28 5 Steady state simulation 34 Acknowledgements 50 Appendix: Proof of Proposition 15 50 References 52 CHAPTER 3 Uniform Random Number Generation Pierre LEcuyer 55 1 Introduction 55 2 Uniform random number generators 5( 3 Linear recurrences modulo in Ml 4 Generators based on recurrences modulo 2 M 5 Nonlinear RNGs 75 6 Empirical statistical tests 76 7 Conclusion, future work and open issues 77 Acknowledgements 78 References 78 vii viii Contents CHAPTER 4 Nonuniform Random Variate Generation Luc Devroye 83 1 The main paradigms 83 2 Uniformly bounded times 94 3 Universal generators 97 4 Indirect problems 99 5 Random processes 107 6 Markov chain methodology 108 Acknowledgement 116 References 116 CHAPTER 5 Multivariate Input Processes Bahar Biller and Soumyadip Ghosh 123 1 Introduction 123 2 Constructing full joint distributions 126 3 Parametric families of joint distributions 133 4 Constructing partially specified joint distributions 136 5 Conclusion 149 References 150 CHAPTER 6 Arrival Processes, Random Lifetimes and Random Objects Lawrence M. Leemis 155 1 Arrival processes 155 2 Generating random lifetimes 167 3 Generating random objects 172 Acknowledgements 178 References 178 CHAPTER 7 Implementing Representations of Uncertainty W. David Kelton 181 1 Introduction 181 2 Random number generation 182 3 Random structure generation 184 4 Application to variance reduction 186 5 Conclusions and suggestions 189 References 191 CHAPTER 8 Statistical Estimation in Computer Simulation Christos Alexopoulos 193 1 Introduction 193 2 Background 195 Contents ix 3 Sample averages and time averages 196 4 Stationary processes 199 5 Analyzing data from independent replications 204 6 Density estimation 210 7 Summary 220 Acknowledgements 222 References 222 CHAPTER 9 Subjective Probability and Bayesian Methodology Stephen E. Chick 225 Introduction 225 1 Main concepts 227 2 Computational issues 237 3 Input distribution and model selection 239 4 Joint input output models 240 5 Ranking and selection 245 6 Discussion and future directions 252 Acknowledgement 253 References 253 CHAPTER 10 A Hilbert Space Approach to Variance Reduction Roberto Szechtman 259 1 Introduction 259 2 Problem formulation and basic results 260 3 Hilbert spaces 263 4 A Hilbert space approach to control variates 27 i 5 Conditional Monte Carlo in Hilbert space 273 6 Control variates and conditional Monte Carlo from a Hilbert space perspective 274 7 Weighted Monte Carlo 275 8 Stratification techniques 278 9 Latin hypercube sampling 281 10 A numerical example 286 11 Conclusions 287 Acknowledgements 288 References 288 CHAPTER 11 Rare Event Simulation Techniques: An Introduction and Recent Advances S. Juneja and P. Shahabuddin 291 1 Introduction 291 2 Rare event simulation and importance sampling 296 3 Rare event simulation in a Markovian framework 302 4 Large deviations of multidimensional random walks 309 x Contents 5 Adaptive importance sampling techniques 316 6 Queueing systems 327 7 Heavy tailed simulations 330 8 Financial engineering applications 335 Acknowledgement 346 References 346 CHAPTER 12 Quasi Random Number Techniques C. Lemieux 351 1 Introduction 351 2 An example 356 3 A key concept: Effective dimension 359 4 Constructing quasi random point sets 364 5 Recurrence based point sets 369 6 Randomization techniques and variance results 371 7 Combination with other variance reduction techniques 374 8 Future directions 375 Acknowledgements 376 References 376 CHAPTER 13 Analysis for Design Ward Whitt 381 1 Introduction 381 2 The standard statistical framework 385 3 The asymptotic parameters for a function of a Markov chain 393 4 Birth and death examples 397 5 Diffusion processes 401 6 Stochastic process limits 405 7 Deleting an initial portion of the run to reduce bias 410 8 Directions for further research 411 Acknowledgement 411 References 411 CHAPTER 14 Resampling Methods R.C.H. Cheng 415 1 Introduction 415 2 The bootstrap 417 3 Quantiles and confidence intervals 422 4 Theory 428 5 Simulation models 436 6 Bootstrap comparisons 443 7 Bayesian models 446 Contents xi 8 Time series output 448 9 Final comments 451 References 451 CHAPTER 15 Correlation Based Methods for Output Analysis David Goldsman and Barry L. Nelson 455 1 Introduction 455 2 Motivation 457 3 Estimators using nonoverlapping batches 459 4 Estimators from overlapping batches 468 5 Summary and conclusions 473 Acknowledgement 474 References 474 CHAPTER 16 Simulation Algorithms for Regenerative Processes Peter W. Glynn 477 1 Introduction 477 2 The steady state simulation problem 478 3 The regenerative estimator for the TAVC 480 4 Choice of the optimal regeneration state 483 5 The regenerative approach to the initial transient and initial bias problems 484 6 When is a simulation regenerative? 487 7 When is a GSMP regenerative? 489 8 Algorithmic identification of regenerative structure 490 9 A martingale perspective on regeneration 493 10 Efficiency improvement via regeneration: Computing steady state gradients 496 11 Efficiency improvement via regeneration: Computing infinite horizon discounted reward 498 References 499 CHAPTER 17 Selecting the Best System Seong Hee Kim and Barry L. Nelson 501 1 Introduction 501 2 Basics of ranking and selection 502 3 Simulation issues and key results 508 4 Example procedures 517 5 Application 521 6 Asymptotic analysis 522 7 Other formulations 526 8 Future directions 53] Acknowledgements 532 References 532 xii Contents CHAPTER 18 Metamodel Based Simulation Optimization Russell R. Barton and Martin Meckesheimer 535 1 Introduction 535 2 Metamodels and simulation 538 3 Metamodel based optimization 545 4 Response surface methodology (RSM) 548 5 Global metamodel based optimization 563 6 Summary 569 Acknowledgements 570 References 570 CHAPTER 19 Gradient Estimation Michael C. Fu 575 1 Introduction 575 2 Gradient based simulation optimization 577 3 Indirect gradient estimation 580 4 Direct gradient estimation 583 5 Examples 598 6 Basic theoretical tools 605 7 Simple guidelines for the simulation practitioner 606 8 Applications 607 9 Probing further 609 10 Future research directions 611 Acknowledgements 612 References 612 CHAPTER 20 An Overview of Simulation Optimization via Random Search Sigrun Andradottir 617 1 Introduction 617 2 A brief review of random search methods 619 3 Convergence 621 4 Efficiency 624 5 Summary 629 Acknowledgements 630 References 630 CHAPTER 21 Metaheuristics Sigurdur Olafsson 633 1 Introduction 633 2 Background to metaheuristics 635 3 Accounting for simulation noise 640 Contents xiii 4 Genetic algorithm 643 5 Tabu search 644 6 The nested partition method 646 7 Making convergence statements 647 8 Future directions 652 References 653 Author Index 655 Subject Index 667
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spellingShingle Simulation
Handbooks in operations research and management science
Computersimulaties gtt
Gestion - Simulation, Méthodes de
Management gtt
Simulation par ordinateur
Simulação larpcal
Systèmes stochastiques
Computer simulation
Management Simulation methods
Stochastic systems
Computersimulation (DE-588)4148259-1 gnd
Operations Research (DE-588)4043586-6 gnd
subject_GND (DE-588)4148259-1
(DE-588)4043586-6
title Simulation
title_auth Simulation
title_exact_search Simulation
title_exact_search_txtP Simulation
title_full Simulation ed. by Shane G. Henderson ...
title_fullStr Simulation ed. by Shane G. Henderson ...
title_full_unstemmed Simulation ed. by Shane G. Henderson ...
title_short Simulation
title_sort simulation
topic Computersimulaties gtt
Gestion - Simulation, Méthodes de
Management gtt
Simulation par ordinateur
Simulação larpcal
Systèmes stochastiques
Computer simulation
Management Simulation methods
Stochastic systems
Computersimulation (DE-588)4148259-1 gnd
Operations Research (DE-588)4043586-6 gnd
topic_facet Computersimulaties
Gestion - Simulation, Méthodes de
Management
Simulation par ordinateur
Simulação
Systèmes stochastiques
Computer simulation
Management Simulation methods
Stochastic systems
Computersimulation
Operations Research
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