Efficient Estimation of Arc Criticalities in Stochastic Activity Networks
提出一种结合蒙特卡洛模拟与节点释放时间精确分析的算法，用于估计随机活动网络中的弧和路径关键性，证明估计量无偏且方差更低，在多种测试网络中效率显著优于标准方法。
An algorithm is described for estimating arc and path criticalities in stochastic activity networks by combining Monte Carlo simulation with exact analysis conditioned on node release times. These estimators are proved to be unbiased and to have lower variance than the corresponding standard Monte Carlo estimators. The algorithm is applied to a variety of standard and randomly generated test networks to establish that the estimators are significantly and robustly more efficient than the standard estimators when run time and statistical efficiency are properly combined.