Hybridization of reinforcement learning and agent-based modeling to optimize construction planning and scheduling

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 liter-ature exists regarding decision-making frameworks with the ability to learn and propose an optimal set of so-lutions 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 hy-bridization of reinforcement learning and simulation approaches to optimize project durations with resource constraints and support construction practitioners in making project planning decision-making.

Automated summary

Decision-making in construction planning and scheduling is complex due to various constraints and uncertainties. This paper proposes a hybrid model that combines reinforcement learning and simulation to optimize project duration, support decision-making, and address the knowledge gap in activity sequencing and work breakdown under uncertainty. The model is demonstrated through case studies of practical construction scheduling problems.