## Object-Oriented Computer Simulation of Discrete-Event SystemsObject-Oriented Computer Simulation of Discrete-Event Systems offers a comprehensive presentation of a wide repertoire of computer simulation techniques available to the modelers of dynamic systems. Unlike other books on simulation, this book includes a complete and balanced description of all essential issues relevant to computer simulation of discrete event systems, and it teaches simulation users how to design, program and exploit their own computer simulation models. In addition, it uses the object-oriented methodology throughout the book as its main programming platform. The reader is expected to have some background in the theory of probability and statistics and only a little programming experience in C++, as the book is not tied down to any particular simulation language. The book also provides 50 complete simulation problems to assist with writing such simulation programs. Object-Oriented Computer Simulation of Discrete-Event Systems demonstrates the basic and generic concepts used in computer simulation of discrete-event systems in a comprehensive, uniform and self-contained manner. |

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### Contents

Getting started | 1 |

11 Basic notions | 2 |

12 Manual simulation | 5 |

13 Activity scanning | 9 |

Event scheduling | 17 |

21 Main paradigm | 18 |

22 ABC approach | 24 |

23 Events versus activities | 25 |

534 Further examples | 112 |

Design of simulation experiments | 115 |

61 Validation of models | 116 |

62 Analysis of variance | 128 |

63 Linear regression | 134 |

Collection and analysis of simulation results | 139 |

71 Gathering of results | 140 |

72 Transient phase characteristics | 144 |

Event lists | 29 |

31 Doubly linked linear list | 30 |

32 Indexed linear list | 35 |

33 Henriksens list | 39 |

34 Twolevel indexed list | 42 |

35 Heaps | 48 |

36 Splay trees | 52 |

37 Runtime efficiency of event scheduling | 58 |

Process interaction | 65 |

41 Basic operations | 66 |

42 Environment in C++ | 68 |

43 Distributed simulation | 77 |

432 Conservative mechanisms | 79 |

433 Optimistic mechanisms | 83 |

Random number generators | 87 |

51 Uniform random variables | 88 |

52 Statistical tests | 95 |

521 Chisquare test | 96 |

522 KolmogorovSmirnov test | 97 |

523 Poker test | 98 |

524 Runs test | 99 |

525 Coupons collector test | 101 |

527 Autocorrelation test | 102 |

53 Nonuniform variate generation | 103 |

531 Inverse transformation method | 105 |

532 Convolution | 107 |

533 Rejection method | 110 |

721 Estimation of mean | 145 |

722 Estimation of variance | 147 |

73 Estimation of steadystate phase characteristics | 148 |

732 Independent replications method | 152 |

733 Method of batch means | 156 |

734 Regenerative method | 158 |

74 Variance reduction methods | 164 |

742 Antithetic variates | 165 |

743 Common random number streams | 168 |

Examples of simulation models | 171 |

81 A simple preemptive system | 172 |

82 A system with priorities and group servers | 178 |

83 Assembly line | 185 |

84 Finitepopulation queuing model | 191 |

85 Satellite communication system | 196 |

Epilog | 207 |

Exercises | 213 |

A Probability distributions | 237 |

A1 The standard normal distribution | 238 |

A2 The Students t distribution | 239 |

A3 The chisquare distribution | 240 |

A4 The F distribution | 241 |

A5 The KolmogorovSmirnov distribution | 245 |

247 | |

255 | |

### Other editions - View all

Object-Oriented Computer Simulation of Discrete-Event Systems Jerzy Tyszer No preview available - 2012 |

Object-Oriented Computer Simulation of Discrete-Event Systems Jerzy Tyszer No preview available - 1999 |

### Common terms and phrases

active ALGORITHM alias method approach array assume attribute average number average waiting confidence interval conveyor coroutine corresponding counter customer arrival determine the average distributed with mean double doubly linked list dummy event Erlang distribution event list event notice event routine event scheduling event-time execute exponentially distributed F distribution formula frequency histogram function given ground station handler histogram implement indicator integers interarrival interval 0,1 job arrival link event link object member variable node normally distributed NULL number of events obtained occur operations parameters passenger phase pointer prev primitive polynomial priority probability processor queue length queuing system random numbers random variable request sample satellite scanning sequence server service completion shown in Fig simulation clock simulation experiments simulation model simulation run Splay trees statistic stop structure Student's t distribution technique to_occur uniformly distributed units variance a2 void