Using path control variates in activity network simulation
WSC '85 Proceedings of the 17th conference on Winter simulation
Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
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Future directions in response surface methodology for simulation
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Control variates for stochastic network simulation
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Derivative estimation with known control-variate variances
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This paper describes a new procedure for using control variates in multiresponse simulation when the covariance matrix of the controls is known. Assuming that the responses and the controls are jointly normal, we develop a new unbiased control-variates point estimator for the mean simulation response. We also compute the covariance matrix of this point estimator in order to construct an approximate confidence-region estimator for the mean response. If the covariances between the responses and the controls are unknown so that the optimal control coefficients must be estimated, then some of the potential efficiency improvement is lost. This loss is quantified in a new variance ratio. We summarize the results of an extensive experimental study in which we apply the proposed estimation procedure to closed queueing networks and stochastic activity networks.