为获得较优跑道调度方案，以提高跑道运行效率，建立了约束多目标多跑道进离 场航班调度模型． 在分析遗传算法与模拟退火算法特征的基础上，提出遗传模拟退火组合 算法． 分别采用 Pareto 支配及理想点法对跑道调度目标函数进行处理，采用惩罚目标函数 值及可行解占优的方式处理约束条件，并确定了不同条件下新粒子更新机制及最优粒子 筛选原则． 文中还通过设置温度自适应改变机制控制算法收敛速度，以提高最优解性能． 最后，以国内某大型机场跑道调度为例，对文中算法的有效性进行验证． 结果表明: 基于 Pareto 支配的约束多目标遗传算法能获得跑道调度多组较优可行解，且时效性强．

In order to obtain an optimal runway-scheduling scheme to improve the efficiency of runway operation，a constrained multi-objective model for arrivals and departures on multi-runways is constructed，and an improved genetic simulated annealing algorithm is proposed by analyzing the characteristics of both the genetic algorithm and the simulated annealing one． In the proposed algorithm，the objective functions for runway scheduling are processed by means of the Pareto dominance and the ideal point method，and the constraint conditions are handled by using the penalty objective functions and the dominated feasible solution． Furthermore，the mechanisms of updating new particles and selecting the best scheme are determined． For the purpose of improving the performance of the optimal solution，the convergence speed of the proposed algorithm is controlled by changing the temperature adaptively． Finally，the effectiveness of the proposed algorithm is verified by the actual runway scheduling of a domestic huge airport． The results show that the proposed algorithm based on the Pareto dominance can obtain a set of better feasible solutions of runway scheduling and is of a better timeliness．