This article addresses the project scheduling and the materials ordering problem (PSMOP) with uncertain activity durations. A two-stage optimisation approach is proposed to generate a robust schedule in order to protect the project execution from the duration uncertainty. First of all, a mixed-integer programming model under a deterministic environment is formulated to address this issue, which aims to minimise the corresponding costs and is solved by the genetic algorithm (GA). Second, a robust project scheduling integrated with materials ordering method (RPSMOM) is developed to build a proactive schedule on the basis of the baseline schedule generated by the given deterministic model. In this method, a novel index that measures the risk of activity delay (RAD) is suggested by taking the uncertain activity durations into account. Finally, computational experiments are conducted under different levels of activity duration uncertainty. The results demonstrate that the integrated proactive scheduling method is highly effective in decreasing the total cost and improving the robustness of the project schedule in most circumstances.

Multi-building, multi-story prefabricated construction projects are notably characterized by high complexity and repetitiveness, which necessitate efficient resource scheduling. Traditional resource-constrained project scheduling problems primarily address global resources, whereas existing studies on repetitive scheduling emphasize crew allocation and often neglect constraints associated with spatially localized resources, such as tower cranes. To address the challenges posed by repetitive prefabricated construction, this study systematically analyzes scheduling characteristics and classifies renewable resources into three categories: local, crew, and global resources. This study also introduces a novel spatial precedence relationship to capture dependencies between activities on adjacent floors. A linear programming model is formulated to minimize both project duration and total resource idle time. The model is developed under several explicit simplifying assumptions to ensure computational tractability while preserving the core-resource interdependencies. The proposed model’s effectiveness is validated through an empirical case study and additional numerical experiments. In the case study, utilization rates for local resources and crews increased by 20% and 8%, respectively. Furthermore, sensitivity analysis of local-resource allocation indicates that increasing the number of tower cranes yields diminishing marginal reductions in project duration, while total resource idle time first decreases and then increases. Consequently, resource over-allocation should be avoided to prevent degradation in utilization.