Natural disasters and cascading infrastructure failures disrupt transportation networks precisely when reliable logistics routing is most critical for delivering relief supplies, medical equipment, and evacuation support. Static shortest-path and classical vehicle-routing heuristics assume a largely intact network and adapt poorly to rapidly evolving road closures, congestion, and demand surges. This study presents an investigation of deep reinforcement learning (DRL) for adaptive, real-time logistics routing under disaster conditions, culminating in a proposed graph-attention multi-agent proximal policy optimization architecture (GAT-MAPPO).
A synthetic disaster-response environment was constructed as a 60-node transportation network subject to stochastic edge disruption, congestion, and time-varying relief-demand surges representing earthquake, flood, hurricane, and combined multi-hazard scenarios. Six baseline routing strategies - static Dijkstra, A* heuristic, Clarke-Wright savings vehicle routing, deep Q-network (DQN), Double DQN, and asynchronous advantage actor-critic (A3C) - were benchmarked against proximal policy optimization (PPO), soft actor-critic (SAC), and the proposed GAT-MAPPO agent across 500 evaluation episodes per method. GAT-MAPPO achieved the highest demand fulfillment rate (93.7%) and lowest mean delivery delay (11.2 min), outperforming the strongest baseline (SAC, 85.1% fulfillment) and the static-routing baseline (58.2% fulfillment) by wide margins.
The proposed agent retained a demand fulfillment rate above 80% at an edge-failure rate of 30%, generalized across all four disaster-scenario types with less than eight percentage points of performance variation, and exhibited graceful degradation under simulated sensor and communication noise. Multi-objective analysis mapped Pareto-optimal trade-offs between delivery time and fleet fuel consumption, and between responder risk exposure and delivery speed, while an ablation of the reward function confirmed that urgency weighting and failed-delivery penalties contributed most to overall performance. These results, illustrate a coherent workflow for developing, benchmarking, and stress-testing DRL-based disaster-logistics routing systems prior to real-world deployment.