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ABSTRACT

In the simulation of a stochastic activity network (SAN), the usual objective is to obtain point and confidence-interval estimators of the mean completion time for the network. This paper presents a new procedure for using path control variates to improve the efficiency of such estimators. Because each path control is the duration of an associated path in the network, the vector of selected path controls has both a known mean and a known covariance matrix. All of this information is incorporated into point- and interval-estimation procedures for both normal and nonnormal responses. To evaluate the performance of these procedures experimentally, we compare actual versus predicted reductions in point-estimator variance and confidence-interval half-length for a set of SANs in which the following characteristics are systematically varied: (a) the size of the network (number of nodes and activities); (b) the topology of the network; (c) the relative dominance (criticality index) of the critical path; and (d) the percentage of activities with exponentially distributed durations. The experimental results indicate that large variance reductions can be achieved with these estimation procedures in a wide variety of networks.

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