Schedule management is an essential part of construction project management. In practical management affairs, many uncertainties may lead to potential project delays and make the schedule risky. To quantify such risk, the Probabilistic Critical Path Method (PCPM) is used to compute the overdue probability. Survey shows it could help project managers understand the schedule better. However, two critical factors limited the application of PCPM: computational efficiency and timeliness. To solve these constraints, we combined subset simulation and statistical learning to build a computationally efficient and dynamic simulation system. Numerical experiment shows that this method can effectively improve the computation efficiency without losing any accuracy and outperforms the other approaches with the same assumptions. Besides, we proposed a machine learning-based way to estimate task duration distributions in PCPM automatically. It collects real-time progress data through user interactions and learns the best PERT-Beta parameters based on these historical data. Our estimator provides our simulation system the ability to handle dynamic assessment without laborious human work. These improvements reduce the limitations of PCPM, making the application of PCPM in practical management affairs possible.

中文翻译：

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使用子集模拟的施工进度动态概率分析

进度管理是建设项目管理的重要组成部分。在实际管理事务中，许多不确定因素可能会导致潜在的项目延误，并使进度具有风险。为了量化此类风险，使用概率关键路径方法 (PCPM) 来计算逾期概率。调查显示，它可以帮助项目经理更好地了解日程安排。然而，两个关键因素限制了 PCPM 的应用：计算效率和及时性。为了解决这些限制，我们结合子集模拟和统计学习来构建计算效率高的动态模拟系统。数值实验表明，该方法在不损失任何精度的情况下有效提高了计算效率，并且优于具有相同假设的其他方法。除了，我们提出了一种基于机器学习的方法来自动估计 PCPM 中的任务持续时间分布。它通过用户交互收集实时进度数据，并根据这些历史数据学习最佳 PERT-Beta 参数。我们的估算器为我们的模拟系统提供了处理动态评估的能力，而无需繁重的人工工作。这些改进减少了PCPM的局限性，使PCPM在实际管理事务中的应用成为可能。