Efficient importance sampling heuristics for the simulation of population overflow in Jackson networks

In this paper, we propose state-dependent importance sampling heuristics to estimate the probability of population overflow in Jackson networks with arbitrary routing. These heuristics approximate the "optimal" state-dependent change of measure without the need for costly optimization involved in other recently proposed adaptive algorithms. Experimental results on tandem, feed-forward and feed-back networks with a moderate number of nodes yield asymptotically efficient estimates (often with bounded relative error) where no other state-independent importance sampling techniques are known to be efficient.