Decision-making in construction planning and scheduling is complex because of budget and resource constraints, uncertainty, and the dynamic nature of construction environments. A knowledge gap in the construction literature exists regarding decision-making frameworks with the ability to learn and propose an optimal set of solutions for construction scheduling problems, such as activity sequencing and work breakdown structure formulations under uncertainty. The objective of this paper is to propose a hybrid reinforcement learning–graph embedding network model that 1) simulates complex construction planning environments using agent-based modeling and 2) minimizes computational burdens in establishing activity sequences and work breakdown formations. Three case studies with practical construction scheduling problems were used to demonstrate applicability of the developed model. This paper contributes to the body of knowledge by proposing the hybridization of reinforcement learning and simulation approaches to optimize project durations with resource constraints and support construction practitioners in making project planning decision-making.

由于预算和资源限制、不确定性以及施工环境的动态特性，施工规划和调度中的决策是复杂的。建筑文献中存在关于决策框架的知识差距，该框架具有学习和提出施工调度问题的最佳解决方案的能力，例如不确定下的活动排序和工作分解结构公式。本文的目的是提出一种混合强化学习-图嵌入网络模型，该模型 1) 使用基于代理的建模模拟复杂的施工规划环境，以及 2) 最大限度地减少建立活动序列和工作分解形成的计算负担。三个具有实际施工调度问题的案例研究被用来证明所开发模型的适用性。本文通过提出强化学习和模拟方法的混合来为知识体系做出贡献，以优化具有资源限制的项目工期并支持建筑从业者进行项目规划决策。