Miguel Molina-Solana

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.

@article{Soman2022,
  author = {Soman, Ranjith~K. and Molina-Solana, Miguel},
  title = {Automating look-ahead schedule generation for construction using linked-data and reinforcement learning},
  journal = {Automation in Construction},
  year = {2022},
  volume = {134},
  articleno = {104069},
  doi = {10.1016/j.autcon.2021.104069},
  comment = {},
  timestamp = {32}
}