An Air–Ground Collaborative Emergency Material Dispatch Method for Wildfires in Dynamic Time-Varying Environments: A Case Study of the High-Altitude Plateau Region in Western China

Wildfires in plateau and mountainous regions are increasingly destructive, often disrupting ground transportation networks and severely constraining emergency logistics, while unmanned aerial vehicles (UAVs) remain limited by payload capacity. To address this challenge, this study proposes an air–ground collaborative emergency material dispatch method for dynamic, time-varying wildfire environments. A multi-layer spatiotemporal network model is developed by incorporating key uncertainties, including fire spread and meteorological fluctuations, into dynamic parameters, and a multi-objective mixed-integer programming framework is established to jointly optimize emergency response time, total dispatch cost, and rescue fairness. To solve the resulting high-dimensional dynamic rescheduling problem, a Fast Ant Colony Optimization-Genetic Algorithm (FACO-GA) integrated with a rolling horizon mechanism is designed. Simulation results under Level 1–10 dynamic perturbations show that, compared with conventional standalone algorithms (GA and ACO), the proposed method demonstrates markedly better robustness and computational efficiency, reducing the extreme average rescheduling response time to 6.80 s, while maintaining a Hypervolume (Hv) retention rate of 96.30% and limiting the Spacing (Sp) change rate to 4.15%. These findings indicate that the proposed approach can effectively overcome computational bottlenecks and provide an adaptive decision-support framework for emergency logistics dispatch in complex wildfire scenarios. Furthermore, comprehensive ablation studies and sensitivity analyses validate the structural necessity of the rolling horizon and ACO modules, ensuring the algorithm’s parameter robustness under extreme stochastic perturbations.