A simulation technique for estimation in perturbed stochastic activity networks

Abstract

We are concerned with what-if analysis in estimating the expected value and the distribution function of completion time in stochastic activity networks. Widely used Monte Carlo simulation models of stochastic networks are often subject to errors caused by the estitnated parameter(s) of underlying input distribution function. " What-if' analysis is needed to establish confidence with respect to small changes in the parameters of the input distributions. However, traditional "what-if'analysis requires a separate simulation run for each input value. Recently, a method based on Likelihood Ratio (LR) for estimating performance function far several scenarios using a single-run extrapolation has been presented. In this paper, we experiment the use of this LR method in a network with exponential arc durations. We also consider the method with a nonlinear control random variate (NCRV) and compare it to crude Monte Carlo. The results show that the NCRV method induces variance reduction and is an effeetive filter to stabilize statistical variation of this single-run estimate.

Data Perturbation Analysis for IS Project Management Based on a Single Time Estimate

“…An alternative to the CPM/PERT is a stochastic activity network, Arsham (1992). This approach assumes the same project is to be performed repeatedly, and there is full knowledge of the time distribution function to be used in Monte-Carlo simulation experiments to approximate the expected CPM/PERT duration.…”