基于交通状态的离场航班动态协同排序方法

为适应协同决策(CDM)需要，考虑空管、航司和机场三方的诉求，对拥挤和非拥挤场景下的离场航班动态协同排序问题进行了系统研究。通过分析离场航班运行特性，利用离场航班的计划撤轮档时间(SOBT)和预计撤轮档时间(EOBT)数据设计了一种离场航班动态排序方法；针对各方构建了离场航班排序的多个优化目标，且为保证排序公平性，提出了航空公司延误公平性评价指标，将非受控离场航班优先级分为3类，对各类非受控离场航班设置其可接受的最大延误时间和最大位置偏移量，创建了基于交通状态的离场航班协同排序模型；采用带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)寻求离场航班动态协同排序的最优解。仿真结果表明:较先到先服务(FCFS)方法，所提方法在2种场景下各排序时段均增加多种排序方案，离场航班总延误均减少50%以上，且在非拥挤场景下提高了航空公司延误公平性。所提方法可对离场航班进行优化排序，显著减少航班延误，有效提升公平性，契合协同决策理念，可实现三方协同排序。 

Dynamic collaborative sequencing for departure flights based on traffic state

To meet the needs for collaborative decision making (CDM), the dynamic collaborative sequencing of departure flights in both crowded and uncrowded scenarios was systematically studied with the demands of air traffic control units, airlines and airports being taken into consideration. With a deep analysis of the operation characteristics of departure flights, a dynamic sequencing method was designed based on scheduled off-block time (SOBT) and estimated off-block time (EOBT) data of the departure flights. Several optimization objectives of departure flight sequencing were set for each of the parties. Meanwhile, an evaluating indicator of airline delay fairness was proposed to ensure the sequencing fairness, the priority of uncontrolled departure flights was divided into three categories, the maximum acceptable delay time and maximum position offset were set for all categories of uncontrolled departure flights, and thus the collaborative sequencing model for departure flights based on traffic state was established. Further, a fast non-dominant sorting genetic algorithm with elite strategy (NSGA-Ⅱ) was designed to find the optimal solution to dynamic collaborative sequencing for departure flights. Simulation results show that, compared with first come first served (FCFS) method, the proposed method adds a variety of sequencing schemes in each sequencing stage in both scenarios, the total delay of departure flights is reduced by more than 50%, and the airline delay fairness is improved in uncrowded scenarios. The proposed method can optimize the sequencing of departure flights, significantly reduce flight delays, effectively improve fairness, and fit in with the concept of collaborative decision making, thus realizing tripartite collaborative sequencing.