Variance reduction techniques in stochastic shortest route analysis: application procedures and results

Abstract

Variance reduction techniques are of much interest to simulation practitioners. However, they typically present formidable difficulties in their implementation, and are, therefore, not widely used. These difficulties stem from the lack of well defined application procedures for the various techniques which are available. This research focuses on the development of application procedures for three variance reduction techniques: antithetic sampling, control variates, and stratified sampling. These procedures are developed for use in stochastic shortest route analysis. The procedures are supported by sound theoretical development which assures that no technique will result in a variance increase. Further, one technique guarantees a reduction in variance of the estimates obtained over direct simulation. We also provide extensive application results over a wide range of network characteristics. The results of these applications indicate that all of the variance reduction techniques investigated work well. Antithetic sampling was found to be far superior to the other techniques and can be generally recommended for use.