航班进场排序是一种典型的NP-hard优化问题，其主要困难在于如何在多约束条件下高效地求解最优调度方案。针对传统模拟退火算法全局搜索能力不足的缺点，提出两阶段生成邻域解策略：第一阶段确定航班降落次序，第二阶段进行跑道使用时刻调度，以满足航班调度多约束的限制。通过调整随温度自适应的最大位置偏移限制，引入交换、反转、倒换算子以进一步增强算法邻域搜索能力。通过多组高峰时段运行数据的测试（求解精确解的时间均大于300 s），所提出的改进算法在全局搜索能力上有效提升，仿真结果在求解效率和求解质量上具有显著优势：与先到先服务方法相比，航班总延误成本减少65%以上；与遗传算法对比，延误成本减少34%以上；与限定300 s计算时间的整数规划模型求解器相比，目标函数求解误差小于1%，求解时间可缩短至1 s内。当不同机型的单位时间延误成本随延误时间变化时，所提出的改进算法优先考虑延误成本提升较大机型次序，以减小总的时间偏差成本。因此，该算法能显著减少航班延误，可为繁忙时段跑道调度提供快速、高效的决策支持。

Flight approach sequencing is a typical NP-hard optimization problem, and its main difficulty lies in how to efficiently solve the optimal scheduling scheme under multi-constraint conditions. Aiming at the shortcomings of the traditional simulated annealing algorithm with insufficient global search ability, a two-stage neighborhood solution strategy was proposed, the first stage determined the flight landing sequence, and the second stage carried out the runway use time scheduling to meet the constraints of flight scheduling. By adjusting the maximum position offset limit that adapted to the temperature, the switching, inverting and inverting operators were introduced to further enhance the neighborhood search ability of the algorithm. Through the test of multiple sets of peak hour operation data(the time to solve the exact solution is greater than 300 s), the proposed improved algorithm can effectively improve the global search ability, and the simulation results have significant advantages in solution efficiency and solution quality: compared with the first-come, first-served method, the total flight delay cost is reduced by more than 65%. Compared with genetic algorithm, the delay cost is reduced by more than 34%. Compared with the integer programming model solver, which limits the calculation time of 300 s, the objective function solution error is less than 1%, and the solution time can be shortened to less than 1 s. When the delay cost per unit time of different models changes with the delay time, the proposed improved algorithm gives priority to the delay cost to increase the order of the larger models to reduce the total time deviation cost. Therefore, the algorithm can significantly reduce flight delays and provide fast and efficient decision support for runway scheduling during peak hours.