Every discrete-event simulation experiment with random input generates random sample paths as output. Each path usually consists of a sequence of dependent observations that serve as the raw material for estimating the true values of one or more long-run performance measures, hereafter called parameters. Two phenomena, sampling error and systematic error, influence how well an estimate approximates the true value of a parameter. Random input induces sampling error, and the dependence among observations often enhances its severity. Systematic error arises from the dependence of the observations on the initially chosen state or conditions in the simulation. The presence of random and systematic errors implies that all estimates need to be interpreted with qualification.
KeywordsQueue Length Sample Path Batch Size Pseudorandom Number Output Analysis
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