Estimating Network Characteristics in Stochastic Activity Networks
提出一种基于拟随机点的蒙特卡洛方法，用于估计随机活动网络中网络完成时间和最短路径时间的分布函数与均值，其估计误差收敛速度优于传统随机抽样。
This paper describes a Monte Carlo method based on the theory of quasirandom points for estimating the distribution functions and means of network completion time and shortest path time in a stochastic activity network. In particular, the method leads to estimators whose absolute errors converge as (log K) N /K, where K denotes the number of replications collected in the experiment and N is the number of dimensions for sampling. This rate compares favorably with the standard error of estimate O(1/K 1/2 ) which obtains for experiments that use random sampling. Moreover, since quasirandom points are nonrandom the upper bound (log K) N /K is deterministic in contrast to the random sampling rate O(1/K 1/2 ) which is probabilistic. The paper demonstrates how the use of a cutset of the network reduces N in the bound when estimating the distribution functions. Two examples illustrate the benefits and costs of using quasirandom points.