AI-driven emergency logistics network deployment in dynamic environments.

Emergency logistics decisions often suffer from inefficiency due to a lack of foresight and robustness in the face of unexpected events. To address this issue, a dynamic deployment model is proposed that combines an Event-Driven Spatio-Temporal Graph Neural Network (STGNN) with Adaptive Robust Optimization (ARO). The Event-Driven STGNN generates probabilistic predictions of logistics network states by integrating historical spatio-temporal data with external event features. The ARO module then converts these uncertain predictions into a structured uncertainty set and solves a two-stage optimization problem to determine emergency resource allocation plans, such as vehicle scheduling, that remain effective even under worst-case scenarios. The model is solved iteratively using a Column-and-Constraint Generation algorithm within a rolling optimization horizon, which allows continuous adaptation to dynamic environments. Model performance is evaluated using the New York City For-Hire Vehicle dataset from 2019 to 2022, which contains more than one billion trip records ( https://www.kaggle.com/datasets/jeffsinsel/nyc-fhvhv-data ). A simulation environment is created that incorporates real-world events, including the COVID-19 pandemic and severe snowstorms. Results show that, in simulated emergency distribution tasks, the proposed model increases demand fulfillment from 71.3% to 96.5% and reduces average delivery delay from 45.2 minutes to 11.8 minutes compared with a benchmark that combines STGNN prediction with deterministic optimization. When compared with advanced models based on standard Robust Optimization, the proposed model improves emergency fleet resource utilization efficiency by 18.2%. The significant performance improvements primarily stemmed from the ARO framework, which provides performance guarantees under worst-case scenarios. Additionally, the synergistic integration of probabilistic forecasting and optimization-based decision-making prevented the model from becoming overly conservative, thereby enhancing resource efficiency. The core contribution of this study lies in establishing and validating a closed-loop framework that systematically integrates data-driven prediction with risk-averse decision-making. This empirically verified paradigm offers an effective approach to emergency logistics deployment under dynamic and uncertain environments.