Dynamic Multimodal Routing With Drones Under Communication Uncertainty in Emergency Logistics: A Hybrid Reinforcement Learning–Heuristic Approach

ABSTRACT

The coordinative use of ground vehicles (e.g., trains and trucks) and drones has emerged as a promising approach to enhance the efficiency and flexibility of emergency logistics. However, communication interference can severely undermine such coordination. To address this challenge, this study investigates a dynamic multimodal routing problem with drones under communication uncertainty in emergency logistics. The duration of the communication interference is considered an uncertain parameter. A mixed‐integer programming model is formulated with a bi‐level objective. The upper‐level objective is to ensure order fulfillment rate, and the lower‐level objective is to minimise cost. To address the computational challenges of this complex problem, a hybrid approach is developed, which integrates reinforcement learning (RL) and adaptive large neighbourhood search (ALNS) within an event‐triggered rolling‐horizon framework. In this approach, RL continuously learns from the real‐time data and guides the ALNS procedure to generate optimal routing decisions dynamically. The proposed approach is evaluated against a benchmark method, demonstrating superior effectiveness and adaptability. Furthermore, a series of experiments are conducted to analyse the effects of numbers of training iteration and the influence of different communication interference durations. The results confirm that the proposed approach improves the efficiency and reliability of emergency logistics under communication uncertainty.