航路资源协同分配的多目标优化方法研究  

A Multi-objective Optimization Method of Collaborative Allocation of Route Resources

作　　者：杨帆[1] 田文[1] 宋津津 YANG Fan;TIAN Wen;SONG Jin-jin(National Key Laboratory of Air Traffic Flow Management,Nanjing University of Aeronautics and Astronautics,Nanjing Jiangsu 210016,China)

机构地区：[1]南京航空航天大学国家空管飞行流量管理技术重点实验室,江苏南京210016

出　　处：《计算机仿真》2021年第11期47-52,共6页Computer Simulation

基　　金：国家自然科学基金(71971112);南京航空航天大学研究生创新基地(实验室)开放基金(kfjj20190717)。

摘　　要：目前,航班飞行需求激增和恶劣天气已经严重影响到航空空域的正常运行,为此针对空域拥堵时航路的时隙航迹资源协同分配进行了研究。根据问题的数学描述,以总延误成本最低和公平损失偏差系数最低为两个目标,建立了航路资源协同分配模型,运用多目标遗传算法(NSGA-Ⅱ)对模型求解分析。通过航路网络的典型数据测试该模型,生成了可供决策者选择的帕累托最优解集,并与传统RBS算法进行比较,实现平均延误成本降低8.5%,平均公平损失偏差系数降低70.6%。结果证明NSGA-Ⅱ算法可以较好的解决航路资源协同分配的问题。In airway network, the excess traffic demand and the influence of weather conditions have become the main reasons for airspace traffic congestion. This paper investigated the collaborative allocation of time slots and trajectories during airspace traffic congestion. According to the mathematical description of the problem, we proposed a collaborative allocation model with the minimum total delay cost and the minimum deviation coefficient of fair loss as two objectives. Then a non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)was applied to solve the problem. By testing the model with typical data of route network, a Pareto optimal solution set selected by decision makers was generated. Comparing the algorithm with the traditional RBS algorithm, the average delay cost was reduced by 8.5%, and the average fair loss deviation coefficient was reduced by 70.6%. The comparison shows that the NSGA-Ⅱ can be well applied in the research of slots-trajectories collaborative allocation.

关 键 词：空中交通流量管理 帕累托解集 多目标遗传算法 航路资源 协同航迹选择程序 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]