Construction projects with outdoor operations are affected by time-varying weather conditions. However, most existing research on stochastic resource-constrained project scheduling problems (SRCPSPs) considers activity duration as a random variable from a time-independent distribution. To address this issue, this study investigates SRCPSP under time-varying weather conditions; an improved estimation of distribution algorithm (EDA) including a ranking and selection method using common random numbers is proposed for enhancing the performance of project scheduling. The benchmark J120 dataset from PSPLIB and a practical case of windfarm construction are used to validate the improved EDA. For three randomly selected cases from the J120 dataset, the improved EDA can reduce the expected makespan by 17.0, 29.4, and 12.5 days when compared with deterministic scheduling. The corresponding makespan reductions obtained by the original EDA are 10.8, 22.7, and 7.1 days. Similarly, the improved EDA obtains 23% higher expected makespan reduction for the practical case.

进行户外作业的建筑项目会受到时变天气条件的影响。但是，有关随机资源受限的项目计划问题（SRCPSP）的大多数现有研究都将活动持续时间视为来自时间独立分布的随机变量。为了解决这个问题，本研究调查了时变天气条件下的SRCPSP。为了提高项目调度的性能，提出了一种改进的分布估计算法（EDA），该算法包括使用通用随机数的排序和选择方法。来自PSPLIB的基准J120数据集和风电场建设的实际案例用于验证改进的EDA。对于从J120数据集中随机选择的三种情况，改进的EDA可以将预期的有效期降低17.0、29.4和12。与确定性计划相比，需要5天。通过原始EDA获得的对应的制造时间减少为10.8、22.7和7.1天。同样，在实际情况下，改进的EDA可使预期的制造时间缩短23％。