Automating look-ahead schedule generation for construction using linked-data based constraint checking and reinforcement learning

Abstract

Look-ahead planning is the stage in construction planning where information from diverse sources is integrated and plans developed for the next six/eight weeks. Poor planning of construction site activities at this stage often results in cost overruns and schedule delays. This work presents a novel Look-Ahead Schedule (LAS) generation method, which uses reinforcement learning and linked-data based constraint checking within the reward, to address the issues associated with manual look-ahead planning and help construction professionals efficiently plan construction activities at this stage. Our proposal can generate conflict-free LAS significantly faster than conventional methods, demonstrating its capability as a decision support tool during look-ahead planning meetings. Therefore, this paper extends existing knowledge in the construction informatics domain by demonstrating the application of reinforcement learning to aid data-driven look-ahead planning.