为充分利用进近空域内的灵活使用空域和空域容量，研究航班进场路径的多目标分配优化问题。考虑安全间隔等约束，构建一种以交通复杂度和航班平均进场时间最小为目标的路径分配模型，设计快速非支配排序遗传算法（non-dominated sorting genetic algorithm-II, NSGA-II）进行优化方案求解。优化结果表明：分配路径后交通复杂度下降了12.23%，平均进场时间下降了14.32%，算法同时分类出需调整进场路径的航班集。对优化方案进行全空域及机场模型软件（total airspace and airport modeller, TAAM）仿真验证。仿真结果表明：路径分配使得在大流量条件下空域内航班瞬时架次提升了10%，扇区仿真负荷降低了9.3%，平均五边落地时间间隔小而均匀，最多降低了18.23%。验证路径分配的可行性。研究成果可有效提高进近空域的利用率、航班进场效率和跑道进场容量，辅助进近空域流量管理或管制员指挥决策。

To fully exploit the flexible use of airspace and its capacity within the arrival airspace, the multi-objective optimization problem of flight arrival paths was studied. Considering constraints such as safety intervals, a path assignment model was constructed with the objectives of minimizing traffic density and average arrival time of flights. A non-dominated sorting genetic algorithm-II(NSGA-II) was designed to solve the optimization problem. The optimization results show that a 12.23% reduction in traffic density and a 14.32% decrease in average arrival time after path assignment. The algorithm simultaneously categorizes a set of flights that required adjustment to their arrival paths. The optimized solution underwent total airspace and airport modeller(TAAM) simulation validation. The results demonstrate that path assignment resulted in a 10% increase in instantaneous aircraft movements within the airspace under high traffic conditions. The sector simulation load decreases by 9.3%, and the average five-sided landing time intervals are small and uniform, with a maximum reduction of 18.23%. The feasibility of path assignment was verified. The research results can effectively enhance the utilization rate of the arrival airspace, flight arrival efficiency, and runway arrival capacity. It assists in the traffic management of the arrival airspace or controller decision-making.