## 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. |

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Page 380

Xd)T be an input random

maintenance-shop example above, d = 2 (in which case X is called bivariate), X,

is the ...

Xd)T be an input random

**vector**of dimension d (AT denotes the transpose of a**vector**or matrix A, so that X is a d X 1 column**vector**). For instance, in themaintenance-shop example above, d = 2 (in which case X is called bivariate), X,

is the ...

Page 382

x. = (AA...,^)r. where Y is multivariate normal N/ji, 2). The marginal distribution of

X, is univariate lognormal LN(/i,-, <t„) where /u, is the ith element of \x. and eru is

the /th diagonal entry in 2. Since the multivariate normal random

x. = (AA...,^)r. where Y is multivariate normal N/ji, 2). The marginal distribution of

X, is univariate lognormal LN(/i,-, <t„) where /u, is the ith element of \x. and eru is

the /th diagonal entry in 2. Since the multivariate normal random

**vector**Y ...Page 480

8.5.2 Multivariate Normal and Multivariate Lognormal The </-dimensional

multivariate normal distribution with mean

covariance matrix 2, where the (/, j')th entry is atJ, has joint density function given

in Sec.

8.5.2 Multivariate Normal and Multivariate Lognormal The </-dimensional

multivariate normal distribution with mean

**vector**\i = (fiy, fi2, . . . , fi.d)T andcovariance matrix 2, where the (/, j')th entry is atJ, has joint density function given

in Sec.

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### Contents

Basic Simulation Modeling | 1 |

FixedIncrement Time Advance | 93 |

Problems | 99 |

Copyright | |

21 other sections not shown

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### 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