Assessing the Efficiency of Variance Reduction Methods in the Construction Project Network Simulation

Abstract

The Monte Carlo simulation has become a standard tool in the practice of planning risk-affected projects. In particular, it is frequently applied to testing the impact of risk on schedule networks with deterministic structures and random activity durations defined by distribution functions of any type. The accuracy of simulation-based estimates can be improved by increasing the number of replications or by applying variance reduction methods. This paper focuses on the latter and analyzes the impact of the variance reduction method on the scale of the standard error of the estimated mean value of project duration. Three methods of variance reduction were examined: the Quasi-Monte Carlo with Weyl sequence sampling, the antithetic variates, and the Latin Hypercube Sampling. The object of the simulation experiment was a sample network model with the activity durations of triangular distributions. This type of distribution was selected as it is often applied in the practice of construction scheduling to capture the variability of operating conditions in the absence of grounds for assuming other types of distribution. The results of the sample simulation provided an indirect proof that applying variance reduction measures may reduce the time of the simulation experiment (reduced number of replications) as well as improve the confidence in the estimates of the model's characteristics.