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We study biased control variates (BCVs), whose purpose is to improve the efficiency of stochastic simulation experiments. BCVs replace the control-simulation mean with an approximation; the resulting control-variate estimator is biased. This bias may not be a significant issue for finite sample sizes, however, because our estimator minimizes the more… (More)
We study control variate estimation where the control mean itself is estimated. Control variate estimation in simulation experiments can significantly increase sampling efficiency, and has traditionally been restricted to cases where the control has a known mean. In a previous paper (Schmeiser, Taaffe, and Wang 2000), we generalized the idea of control… (More)
W e develop a numerically exact method for evaluating the time-dependent mean, variance, and higher-order moments of the number of entities in a Ph t /Ph t / queueing system. We also develop a numerically exact method for evaluating the distribution function and moments of the virtual sojourn time for any time t; in our setting, the virtual sojourn time is… (More)
W e demonstrate a numerically exact method for evaluating the time-dependent mean, variance, and higher-order moments of the number of entities in the multiclass Ph t /Ph t / K queueing network system, as well as at the individual network nodes. We allow for multiple, independent, time-dependent entity classes and develop time-dependent performance measures… (More)
A statistically efficient method for performing simulation experimentation of nonstationary queueing models is outlined. The method utilizes a nonstationary queueing approximation as an external control variate system. A simple nonstationary tandem queueing model serves as an example of this variance reduction method.