This study proposes an edge computing–based dynamic routing optimization framework to address high decision delay and poor adaptability in centralized emergency medical supply distribution during public health emergencies. Such events often cause a 300%–500% surge in medical supply demand, exposing system vulnerabilities. The framework deploys a closed-loop “sensing–prediction–optimization” mechanism at the network edge. A hierarchical analysis method quantifies the dynamic urgency of each demand point as a penalty weight in the optimization objective, while a Transformer–GRU hybrid predictor at edge nodes estimates real-time travel time and demand intensity. A proximal policy optimization (PPO) reinforcement learning algorithm enables low-latency rolling route replanning with heuristic refinement. Simulation results show an F1 score of 0.914 (95% CI 0.892, 0.936) in on-time delivery discrimination, with an AUC of 0.967 and a top-5 NDCG of 0.934, outperforming baseline models. Compared with centralized architectures, response latency is reduced by 95.29% and weighted tardiness by 48.64%. Task completion remains above 95.5% under 50% congestion and 20% new orders, demonstrating strong robustness and the potential of edge computing and AI for resilient medical logistics systems.
Lina Guo (2026) studied this question.