为了提升航班运行效率并减轻延误的负面影响，深入分析了航班延误的核心成因，涵盖了天气信息的不确定性、航班状态波动以及起飞时段的拥堵现象。随后，融合反向学习策略、混沌局部搜索策略与交叉变异策略于星雀算法之中，设计了一种离港航班调度优化算法。同时，构建了以最小化延误总成本为目标的离港航班调度优化模型，为航班调度提供了科学依据与指导。通过在咸阳机场的实际场景中，对18 h内离场航班进行优化排序实验验证，结果显示：与粒子群算法、双交叉遗传算法、蚁群算法及星雀算法相比，在最差情况下的延误代价至少减少80%，同时，在平均延误代价上实现至少70%的降低，并且在方差上达到至少90%的显著降低。这一成果表明，本文方法在提升航班调度效率与减少延误方面具有显著优势。

In order to enhance the efficiency of flight operations and mitigate the negative impact of delays. The core causes of flight delays were analyzed in depth, covering the uncertainty of weather information, the fluctuation of flight status, and the congestion phenomenon during the departure time. Subsequently, a departure flight scheduling optimization algorithm was designed by integrating the inverse learning strategy, chaotic local search strategy and cross-variance strategy in the nutcracker algorithm. At the same time, a departure flight scheduling optimization model was constructed with the objective of minimizing the total delay cost, which provided a scientific basis and guidance for flight scheduling. Through the experimental validation of the optimal sequencing of departing flights within 18 hours in the real scenario of Xianyang airport, the results show that compared with the particle swarm optimization algorithm, the double-crossover genetic algorithm, the ant colony optimization algorithm, and the nutcracker algorithm, a reduction of at least 80% of the delay cost is achieved in the worst case scenario, and a reduction of at least 70% of the average delay cost, and achieves a significant reduction of at least 90% in variance. The result shows that our method has significant advantages in improving flight scheduling efficiency and reducing delays.