Real-Time Resource Allocation Optimization for Dynamic Construction Job Sites Using Deep Reinforcement Learning: A Case Study Implementation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I3P103Keywords:
Terms Deep reinforcement learning, construction management, resource allocation, real-time optimization, multi-agent systemsAbstract
This paper presents a comprehensive deep reinforcement learning (DRL) framework for real-time resource allocation optimization in dynamic construction environments. Traditional construction management methods result in 85% of projects exceeding budgets by an average of 28% [1], while our DRL approach demonstrates 15-25% improvements in resource utilization efficiency and 7-15% cost reductions. We implement and evaluate multiple DRL algorithms including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradient (DDPG) using a real-world case study of the Austin Central Business District Mixed-Use Development project. Our hybrid state-action space design incorporates real-time IoT sensor data, safety constraints, and multi-objective optimization across cost, schedule, and quality metrics. The proposed system achieved convergence within 2,000-4,000 training episodes and demonstrated stable performance in dynamic environments with 30-50% reduction in safety incidents. Results show significant potential for transforming construction project management through intelligent resource allocation systems
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