3 research outputs found
Analysis of variance reduction techniques in various systems
Cataloged from PDF version of article.In this thesis, we consider four different Variance Reduction Techniques (VRTs):
Antithetic Variates (AV), Latin Hypercube Sampling (LHS), Control Variates (CV),
and Poststratified Sampling (PS). These methods individually or in combination are
applied to the steady state simulation of three well-studied systems. These systems are
M/M/1 Queuing System, a Serial Line Production System, and an (s,S) Inventory
Policy. Our results indicate that there is no guarantee of a reduction in variance or an
improvement in precision in estimates. The performance of VRTs totally depends on the
system characteristics. Nevertheless, CV performs better than PS, AV and LHS on the
average. Therefore, instead of altering the input part of the simulation, extracting more
information by CV should be more effective. However, if any extra information about
the system is not available, AV or LHS can be favored since they do not require
additional knowledge about the system. Furthermore, since the analysis of output data
through CV or PS requires a negligible time compared to the simulation run time,
applying CV and PS at all possible cases and then selecting the best one can be the best
strategy in the variance reduction. The use of the combination of methods provides
more improvement on the average.Çelik, SabriM.S
A splitting scheme for control variates
This paper details a new control-variate splitting scheme yielding an unbiased estimator of the mean response and an unbiased estimator of the variance of the first estimator. This scheme also yields an asymptotically exact confidence interval for the mean response. We present analytical and empirical performance comparisons of this scheme versus other control-variate procedures