Dynamic optimization of multi-truck and multi-drone collaborative delivery for emergency material distribution

Emergency material distribution, a crucial component of post-disaster emergency rescue, presents several challenges, including high distribution risk, complex road networks, halted traffic, and urgent demand. Even though vehicles have a large payload capacity, single-truck distribution models sometimes fall short of the strict timing requirements of emergency response. On the other hand, drone-based delivery systems show clear benefits in terms of accessibility, flexibility, and operational efficiency. The truck–drone synergistic distribution mode offers a unique means of significantly enhancing the efficiency of emergency supply delivery through resource integration and complementary advantages. To address this, a multi-vehicle, multi-drone, multi-circuit emergency material synergistic distribution path optimization model was created. The model aims to minimize the maximum delivery time among all trucks, while accounting for practical issues like the restriction of vehicle traffic following the disaster, the satisfaction of multiple demand points per flight, and the synergistic constraints of trucks and drones. As a solution to this model, the hybrid adaptive genetic algorithm (HAGA), incorporating dynamic programming, was proposed. The Solomon test case's R206, C206, and RC206 data sources were used to evaluate the algorithm's efficacy. Regardless of small, medium, or large-scale cases, the results demonstrate that the HAGA can greatly increase the efficiency of emergency material distribution following a disaster, with a maximum improvement of 15.41% in distribution efficiency and a significant improvement in computational efficiency, with a maximum reduction of 49.04% in computational time. According to the sensitivity analysis of the drone parameters, the optimal configuration of flight time and speed significantly improves the system performance. The proposed method provides insights for low-altitude economic applications and emergency logistics decision-making.