基于Monte-Carlo模拟的进场排序不确定性研究

An Uncertainty Analysis of Arrival Aircraft Schedule Based on Monte-Carlo Simulation

为了提升终端区航班排序抵御不确定因素扰动风险的能力,保证航班运行效率及降低延误损失,综合考虑终端区运行的多种约束限制。基于不确定性因素对进场航班影响分析,以航班总延误和管制干预最小为目标,建立进场航班排序的多目标随机期望值模型。应用Monte-Carlo模拟方法刻画航班运行状态随机变量的统计特征,将模型所求目标函数值表征为随机模拟的数学期望,设计了带精英策略的非支配排序遗传算法(NSGA-II)寻求模型的Pareto最优解集,绘制不同阈值的缓冲间隔下得出Pareto前沿拟合曲线。采用广州终端区典型时段进场航班数据进行仿真验证,结果表明模型中不同阈值范围的缓冲间隔设置,可提供权衡航班延误和管制干预之间的合理化建议,且最高可降低32.4%的航班延误。所提方法能有效缓解繁忙机场航班延误,提升航空运输服务能力。

In order to improve the robustness of sequencing of arrival aircrafts in terminal areas against unexpected disturbances,ensure the efficiency of flights and reduce the delays〉 multiple constraints in terminal areas arc considered and a scheduling algorithm for arrival aircrafts is proposed. With an analysis on the critical uncertainty factors related to arrival aircrafts, a scheduling model based on multi-objective stochastic expected value is constructed 〉 which minimizes total flight delays and regulatory interventions. For solving this optimization problem, a Non-dominated Sorting Genetic Algorithm （NSGA-II） is designed, using the elitist strategy for Pareto optimal solution search. A Monte-Carlo simulation is applied to study the statistical characteristics of the random variables related to the operation conditions of flights, and evaluate the objective function via its expected values through a random simulation procedure. Pareto frontier fitting curves for a range of Uncertainty Buffer arc plotted. As a numerical simulation and verification for the proposed algorithm 〉 the data of arrival aircrafts during typical periods in terminal areas of Guangzhou International Airport arc collected. Using this proposed model with multiple thresholds for Uncertainty Buffer, it turns out to provide reasonable proposals for balancing flight delays and controller interventions. In particular, it can reduce flight delays by 32.4 % at a maximum. rrhc results imply that the proposed method can effectively reduce flight delays in busy airports and enhance the robustness of aviation services.