基于多目标帝国竞争算法的进场排序与调度

为兼顾空管、机场、航司以及民众的不同诉求，提出了基于多目标帝国竞争算法的进场排序与调度方法，达到平衡交通需求与进场管理的目的。首先，借鉴机器调度领域研究成果，梳理与精简进场排序与调度的评价指标，并结合运行约束构建多目标进场排序与调度模型。接着，引入非支配排序，设计多目标帝国竞争算法，给出衡量帕累托解集优劣的评价指标。最后，采用通用数据集与长沙黄花机场实际运行数据实施案例进行仿真与验证。结果表明：提出的多目标帝国竞争算法，相对于带精英策略的非支配排序遗传算法以及多目标模拟退火算法而言，解集更占支配地位、分布更均匀、收敛性更好，求解的效率也更高；提出的算法能有效实现进场排序与调度，即便以标准间隔的1.8倍实施仿真，总延误时间、总飞行时间和最大飞行时间，相对于实际运行分别降低了41.2%、11.4%和8.6%。

Arrival sequencing and scheduling based on multi-objective Imperialist competitive algorithm

A new method of arrival sequencing and scheduling is proposed in this paper based on the multi-objective Imperialist competitive algorithm. This method can simultaneously consider different demands of air traffic control, airports, airlines, and the public, therefore balancing traffic demands and arrival management. The evaluation indicators of the arrival sequencing and scheduling are firstly combed and simplified, drawing on the research achievements in the field of machine scheduling, followed by the construction of a multi-objective arrival sequencing and scheduling model by combining the operating constraints. A Multi-Objective Imperialist Competitive Algorithm (MOICA) is then presented by introducing the non-dominated sorting strategy, and performance indexes are also provided to evaluate the pros and cons of Pareto solutions. Finally, a set of benchmark instances and the actual operation data of Changsha Huanghua International Airport are used to implement case simulation and verification, and comparison is made with the commonly used multi-objective algorithms such as NSGA-II or MOSA. The compared results exhibit a dominant position of the proposed MOICA with more uniform distribution, better convergence, and higher quality of the solution set. The proposed algorithm is also more efficient. Additionally, the proposed method can effectively realize the arrival sequencing and scheduling for the real case. Even when the simulation is performed at 1.8 times of the standard interval, the total delay time, total flight time, and maximum flight time are reduced by 41.2%, 11.4%, and 8.6%, respectively, relative to the actual operation.