基于点融合系统的多目标进场排序与调度

持续增长的交通需求和日趋饱和的空域资源对飞行安全和管制效率要求更高，鉴于此，研究基于点融合系统（PMS）的多目标进场排序与调度问题。分析四维航迹预测模型与方法，实现四维航迹预测功能。针对PMS的运行模式和多个利益相关方的需求，构建PMS多目标排序与调度模型，提出基于多目标帝国竞争算法（ICA）。利用长沙黄花国际机场实际运行数据与蒙特卡罗模拟数据对优化模型与算法进行验证。结果表明：所提算法有良好的实际应用效果，能为管制员提供决策支持；在应用基于PMS的多目标进场排序与调度，即使在相对保守的安全间隔下，相对于实际运行优化后的总延误时间、总飞行时间、最大飞行时间分别有70.8%、13.2%、11.8%的缩减。

Multi-objective arrival sequencing and scheduling based on point merge system

Increasing traffic demand and the saturated terminal airspace call for safer flight performance and more efficient control performance. Therefore, this paper focuses on the multi-objective arrival sequencing and scheduling based on point merge system (PMS). Firstly, the four-dimensional trajectory prediction model was developed to predict future trajectories since trajectory prediction played a critical role in arrival sequencing and scheduling. Secondly, the PMS-based multi-objective arrival sequencing model was put forward while taking both the PMS operation modes and different stakeholders appeals into account. Moreover, the multi-objective imperialist competition algorithm (ICA) was proposed based on a non-dominated sorting strategy for solving such a problem. Finally, the data from the Monte Carlo simulation and actual operating of Changsha Huanghua International Airport were used for validation. The results indicated that the proposed method was effective and efficient. On the one hand, the proposed multi-objective ICA did well in practical application, making decision support for controllers. On the other hand, the proposed method could reduce the total delay time, total flight time, and maximum flight time by 70.8%, 13.2%, and 11.8%, compared with the actual operation, with relatively conservative safety separations.