Dynamic Real-Time Optimization of Modular Unit Allocation to Off-Site Facilities in Postdisaster Reconstruction Using Deep Reinforcement Learning

Postdisaster housing reconstruction (PDHR) requires robust, efficient planning, and coordination among dispersed prefabrication facilities and jobsites to maximize prefabrication benefits. Manual construction methods and inherent risks often lead to unforeseen incidents and delays. Previous off-site construction studies focused on specific factors, neglecting uncertainties and the improvement of the Social Vulnerability Index (SoVI). Considering all critical parameters, this study proposes a real-time optimized allocation using deep reinforcement learning (DRL) for PDHR projects. The framework employs the Q-learning algorithm to generate the best real-time schedules for each prefabrication facility based on a current work status to complete a planned project. This is illustrated through a case study with ten project sites, four types of module layouts, and four prefabrication facilities. To demonstrate the superiority of the DRL-based method, the model was compared to the Monte Carlo Simulation and the Genetic Algorithm (GA) for the nine criteria related to time, cost, and social vulnerability. The DRL-based algorithm optimized the time parameter by reducing delay by 33.6% compared to the Monte Carlo simulation and by 46.4% compared to the GA. Similarly, it reduced missed deadlines by 35.1% compared to the Monte Carlo Simulation and by 13.3% compared to the GA. When comparing the cost parameter, the DRL model reduced overall cost by 3.4% compared to the Monte Carlo simulation and by 18.6% compared to the GA. In addition, it was able to prioritize the more vulnerable jobsites over less vulnerable ones to reduce delays and missed deadlines. In this regard, the proposed approach contributes to the body of knowledge by introducing a new automated model for PDHR project work distribution, considering productivity, cost, time, resources, uncertainties, and SoVI, thereby improving informed decision-making and overall project performance.