Titlebook: Discrete-Event Simulation; Modeling, Programmin George S. Fishman Textbook 2001 Springer Science+Business Media New York 2001 Analysis.Disc

Keshan-disease

生命層

Stefan Schaltegger,Roger Burritt proportion of its running time executing searches for new space and creating and maintaining order among the myriad of entity records and event notices it generates as simulated time evolves. In spite of their relative importance, current PC workstation environments, with their substantial memories

EXALT

Yoshihiro Ito,Hiroyuki Yagi,Akira Omorindent observations that serve as the raw material for estimating the true values of one or more long-run performance measures, hereafter called .. Two phenomena, . and ., influence how well an estimate approximates the true value of a parameter. Random input induces sampling error, and the dependenc

千篇一律

Stefan Schaltegger,Roger Burritt delay time, queue length, and resource utilization. Sample averages summarize these data and usually become the focal point of reports and presentations to management. Since they merely approximate corresponding long-run averages that would be observed if the simulation were run for an infinite rat

Aesthete

compassion

粗野

macrophage

Sampling from Probability Distributions,stributions. Often, this incorporation is relatively seamless, requiring the user merely to pull down a menu of options, select a distribution, and specify its parameters. This major convenience relieves the user of the need to write her or his code to effect sampling.

可耕種

Pseudorandom Number Generation,e of i.i.d. random samples from .(0, 1) as input. To meet this need, every discrete-event simulation programming language provides a . that produces a sequence of nonnegative integers .., ..,... with integer upper bound . > .. ?. and then uses .., ..,..., where .. := ../., as an approximation to an i.i.d. sequence from .(0, 1).

LEERY

Preparing the Input,ulation models and executable programs, but only after we have identified all sources of stochastic variation, specified sampling distributions that characterize each source, and assign numerical values to the parameters of the distributions. This chapter addresses these issues.