## Simulation Modeling and AnalysisSince the publication of the first edition in 1982, the goal of Simulation Modeling and Analysis has always been to provide a comprehensive, state-of-the-art, and technically correct treatment of all important aspects of a simulation study. The book strives to make this material understandable by the use of intuition and numerous figures, examples, and problems. It is equally well suited for use in university courses, simulation practice, and self study. The book is widely regarded as the “bible” of simulation and now has more than 100,000 copies in print. |

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### Contents

1 | |

Chapter 2 Modeling Complex Systems | 85 |

Chapter 3 Simulation Software | 181 |

Chapter 4 Review of Basic Probability and Statistics | 214 |

Chapter 5 Building Valid Credible and Appropriately Detailed Simulation Models | 246 |

Chapter 6 Selecting Input Probability Distributions | 279 |

Chapter 7 RandomNumber Generators | 393 |

Chapter 8 Generating Random Variates | 426 |

Chapter 10 Comparing Alternative System Configurations | 556 |

Chapter 11 VarianceReduction Techniques | 587 |

Chapter 12 Experimental Design and Optimization | 629 |

Chapter 13 AgentBased Simulation and System Dynamics | 693 |

Appendix | 721 |

References | 725 |

Index | 759 |

Colour Plates | 778 |

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### Common terms and phrases

90 percent confidence acceptance-rejection method algorithm alias method approach approximately average delay batch means Chap configurations continuous random variable correlation covariance-stationary define delay in queue density function design points discrete discussed in Sec distribution function event list event type example exponential distribution factor factorial design FIFO FIGURE fprintf(outfile fractional factorial designs gamma gamma distribution given histogram idle initial input integer interaction interarrival inverse-transform method large number LCGs lognormal M/M/1 queue machine metamodel Note number in queue number of customers observations obtain percent confidence interval plot Poisson process Prob probability procedure queueing model queueing system random numbers random variables sample mean sampst scale parameter scheduled server sim_time simlib simulation model simulation packages specified station statistical stochastic stochastic process Suppose Table teller timest tion valid variance variates vector warmup period Weibull distribution Xi’s