## Simulation modeling and analysisThis authoritative, comprehensive, and thoroughly up-to-date guide addresses all the important aspects of a simulation study, including modeling, simulation languages, validation, input probability distribution, and analysis of simulation output data. Full scale treatments of manufacturing systems simulation and simulation software and animation are also included along with useful and instructive case studies. |

### From inside the book

Results 1-3 of 37

Page 247

4.3 SIMULATION OUTPUT DATA AND

simulation models use random variables as input, the simulation output data are

themselves random, and care must be taken in drawing conclusions about the ...

4.3 SIMULATION OUTPUT DATA AND

**STOCHASTIC PROCESSES**Since mostsimulation models use random variables as input, the simulation output data are

themselves random, and care must be taken in drawing conclusions about the ...

Page 379

In yet other cases, we may want to specify an entire input

Sec. 4.3) in which the marginal distribution of the individual random variables

composing the process is to be specified, as well as the autocorrelations

between ...

In yet other cases, we may want to specify an entire input

**stochastic process**(seeSec. 4.3) in which the marginal distribution of the individual random variables

composing the process is to be specified, as well as the autocorrelations

between ...

Page 389

Let N(t) = max{i: r, ^ t } be the number of events to occur at or before time t for t ^ 0.

We call the

purposes, the events of interest are usually arrivals of customers to a service

facility of ...

Let N(t) = max{i: r, ^ t } be the number of events to occur at or before time t for t ^ 0.

We call the

**stochastic process**{N(t), t ^ 0} an arrival process since, for ourpurposes, the events of interest are usually arrivals of customers to a service

facility of ...

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Basic Simulation Modeling | 1 |

FixedIncrement Time Advance | 93 |

Problems | 99 |

Copyright | |

21 other sections not shown

### Other editions - View all

### Common terms and phrases

acceptance-rejection method algorithm alias method approach average delay batch Chap confidence interval configurations correlation covariance-stationary define delay in queue density function discrete discussed in Sec distribution function estimate event list event type example exponential distribution exponential random factors FIFO FIGURE forklift FORTRAN fprintf(outfile gamma gamma distribution given histogram idle independent initial integer interarrival inverse-transform method job type machine manufacturing system mean metamodel Note number in queue number of customers observations obtain output data percent confidence interval plot Poisson process Prob probability procedure Q-Q plot queueing model queueing system random numbers random variables replications sample sampst scale parameter scheduled server sim_time simlib simulation simulation model simulation packages simulation run simulation study single-server queueing specified station statistical steady-state stochastic stochastic process Suppose Table teller throughput tion update valid variance variates vector Weibull Weibull distribution