为研究无人驾驶航空器与有人驾驶航空器终端区融合运行的问题，针对无人机的数据链路延迟特性，结合尾流因素、通信、导航和监视误差，建立了改进的Event空中碰撞模型计算有人／无人运行间隔，以延误和效率为目标建立终端区进场航班排序与调度的模型，设计基于滑动时间窗的精英保留策略的自适应快速非支配排序遗传算法（RHC-NSGA-Ⅱ），计算进场航班排序与调度的最优解。研究对象选取轻型、中型和重型机作为组合，求解满足ICAO提出的安全目标水平的纵向安全运行间隔，通过某机场的实际运行数据采用改进的算法对进场航班排序与调度模型进行验证。仿真结果表明，改进的NSGA-Ⅱ方法相较于NSGA-Ⅱ算法，算法性能更优越，求解的效率更高；针对大规模数据集，对比25%混合度的有人／无人运行和50%混合度的有人／无人运行，总延误时间和平均延误时间相对于传统有人机运行都提高了0.6%，总飞行时间和最大飞行时间则分别降低了1.32%、0.96%。

An improved Event air collision model was established to calculate CA (conventional aircraft)/RPA (remotely piloted aircraft) integrated operation intervals. This model considers the data link latency characteristics of RPAs, while considering wake turbulence, communication, navigation, and surveillance errors. In order to investigate the problem of integrated operation of CA/RPA terminal area, a method of arrival sequencing and scheduling in terminal area was established. A NSGA-Ⅱ (non-dominated sorting genetic algorithms-Ⅱ) based on RHC(receding horizontal control) with the elite retention strategy was then presented by introducing the adaptive strategy to calculate the optimal solution for approach flight sequencing and scheduling. Light, medium and heavy aircrafts were selected as combinations for the research object to solve the longitudinal safe operation intervals that satisfy the safety target level proposed by ICAO. Finally, the actual operation dataset of an airport was used to implement case simulation and verification, and comparison was made with the primitive NSGA-Ⅱ. The simulation results show that the improved NSGA-Ⅱ method has superior algorithmic performance and more efficient solution compared to the primitive NSGA-Ⅱ algorithm; for the large-scale dataset, comparing the manned/unmanned operation with 25% mixing degree and the CA/RPA operation with 50% mixing degree, the total delay time and the average delay time are both improved by 0.6% compared to the traditional manned operation, and the total flight time and the maximum flight and maximum flight time were reduced by 1.32% and 0.96%, respectively.