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.
Original language | English |
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Article number | 104069 |
Number of pages | 16 |
Journal | Automation in Construction |
Volume | 134 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
Keywords
- Lean construction
- Linked-data
- Look ahead schedule (LAS)
- Look-ahead planning
- Q-learning
- Reinforcement learning
- Resource constrained project scheduling problem (RCPSP)
- Scheduling