An empirical evaluation of antithetic variates and quasirandom points for simulating stochastic networks

Abstract

This paper is concerned with simulation procedures for estimating the distribution function of the time to complete stochastic activity networks. A conditional Monte Carlo method using uniformly directed cutsets is considered and combined with antithetic variate sampling and quasirandom points. The efficiency of the two variance reduction techniques is compared using CPU times and mean- square errors. By using three examples the study illustrates the decreasing benefits of using quasirandom points over antithetic variate technique as the network size increases.