Simulation of autonomous resource allocation through deep reinforcement learning-based portfolio-project integration
Document Type
Article
Publication Date
6-1-2024
Abstract
Resource allocation has always been a critical challenge for construction project planning, and it affects the cost, duration, and quality of the projects. However, current methods mainly focus on a single project and lack integrated planning and optimization across a construction company's multiple projects. This paper describes a simulation of an Autonomous Resource Allocation (ARA) model using Deep Reinforcement Learning (DRL) agents and methods like Double Deep Q-Networks and combined experience replay to develop and test ARA algorithms based on data harvesting from the Internet of Things (IoT) devices. The results show that DRL can successfully perform ARA by capturing the complex interactions among resource allocation features, without needing retraining when situations change. It shows promising future possibilities for construction companies to improve resource utilization and project performance for larger and more complex construction projects.
Publication Source (Journal or Book title)
Automation in Construction
Recommended Citation
Soleymani, M., Bonyani, M., & Wang, C. (2024). Simulation of autonomous resource allocation through deep reinforcement learning-based portfolio-project integration. Automation in Construction, 162 https://doi.org/10.1016/j.autcon.2024.105381