机场地面保障车辆调度是机场地面运行的关键环节，为提升地面保障效率，针对机场地面保障车辆调度问题，考虑航班时刻信息的变化以及计划外的新增需求，提出基于两阶段法的机场地面保障车辆调度模型，设计改进遗传算法进行求解。根据南京禄口机场的实际运行数据，以具有顺序约束的摆渡车和加油车为例进行仿真实验。实验结果表明：与先到先服务与传统遗传算法得到的结果对比，使用车辆数目分别降低40.0%与33.3%，保障总时间分别降低41.4%和16.1%，与静态调度结果进行对比，保障服务时间增加3.1%仍在合理范围内，其余指标出现明显变化。可见机场保障车辆动态调度模型能够提高地面运行调度效率，减少航班延误率。

The dispatching of airport ground support vehicles is a critical component of airport ground operations. To enhance ground support efficiency, a two-stage model for airport ground support vehicle scheduling was proposed, addressing changes in flight schedules and unplanned additional demands. An improved genetic algorithm was designed to solve the problem. Using actual operational data from Nanjing Lukou International Airport, simulation experiments were conducted with shuttle buses and refueling trucks under sequential constraints as examples. The experimental results indicate that, compared with first-come-first-served and traditional genetic algorithm approaches, the proposed method reduces the number of vehicles used by 40.0% and 33.3%, respectively, and decreases the total support time by 41.4% and 16.1%, respectively. Compared with static scheduling results, the support service time increases by 3.1%, which is still within a reasonable range, while other metrics show significant improvements. These findings demonstrate that the dynamic scheduling model for airport ground support vehicles can improve ground operation scheduling efficiency and reduce flight delay rates.