Stochastic resource-constrained project scheduling problem with time varying weather conditions and an improved estimation of distribution algorithm

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.