【摘要】 针对大型枢纽机场近机位资源紧张与现有分配方法对航班不确定性响应不足的问题，提出一种面向大规模停机位的预分配分层优化方法。构建兼顾航空公司偏好、旅客服务质量与机场运行效率的混合整数规划模型，并引入预计入位时间预测结果以优化机位占用时间，提升对计划偏差的鲁棒性。为求解所建模型，设计第二代非支配排序遗传算法，并以上海浦东机场为实例开展实证验证。结果显示，方法可使靠桥率提升5%，滑行距离减少7.85%，人工调整次数显著减少。表明方法能够有效处理大规模航班信息，具有较强的实际应用价值。更多还原

【Abstract】 To address the challenge of limited contact gate availability and the inadequate responsiveness of existing assignment strategies to flight schedule uncertainties at major hub airports, a hierarchical preallocation optimization approach is developed for large-scale gate assignment. A mixed-integer programming model is formulated to simultaneously account for airline preferences, passenger service quality, and airport operational efficiency. Unlike conventional methods that rely on scheduled arrival times, this study incorporates predicted estimated in-block time(EIBT) to optimize gate occupancy schedules and enhance robustness to arrival deviations. To solve the proposed model, a second-generation non-dominated sorting genetic algorithm(NSGA-II) is designed. An empirical analysis based on Shanghai Pudong International Airport demonstrates that the proposed approach improves the contact gate assignment rate by 5%, reduces total taxiing distance by 7. 85%, and significantly decreases the frequency of manual adjustments.The results indicate that this method can effectively handle large-scale flight information, and has strong practical application value.更多还原