Variance reduction in queueing simulation using generalized concomitant variables

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Abstract
To improve the efficiency of system performance estimators generated by a queueing simulation, procedures are developed for exploiting standardized concomitant variables that are associated with each input process sampled during the simulation. A multivariate central limit theorem is established for such variables in a broad class of regenerative queueing systems. This result is the basis for robust, asymptotically stable variance reduction techniques that incorporate these concomitant variables into poststratified sampling schemes as well as control-variate schemes. Each procedure is adapted to estimation methods based on replication analysis and regenerative analysis. A summary of the results of an experimental performance evaluation indicates the potential efficiency gains that can be achieved with these procedures