|
Abstract : |
We propose a new estimator of steady-state blocking probabilities for simulations of stochastic loss models that can be much more e#cient than the natural estimator (ratio of losses to arrivals). The proposed estimator is a convex combination of the natural estimator and an indirect estimator based on the average number of customers in service, obtained from Little's law (L = #W). It exploits the known o#ered load (product of the arrival rate and the mean service time). The variance reduction is dramatic when the blocking probability is high and the service times are highly variable. The advantage of the combination estimator in this regime is partly due to the indirect estimator, which itself is much more e#cient than the natural estimator in this regime, and partly due to strong correlation (most often negative) between the natural and indirect estimators. In general, when the variances of two component estimators are very di#erent, the variance reduction from the optimal convex combination is about 1, |
