交通信息与安全2016，Vol.34Issue(4)：22-28,7.DOI:10.3963/j.issn1674-4861.2016.04.004
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
摘要
Abstract
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 are 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 are plotted.As a numerical simulation and verification for the proposed algo-rithm,the data of arrival aircrafts during typical periods in terminal areas of Guangzhou International Airport are collect-ed.Using this proposed model with multiple thresholds for Uncertainty Buffer,it turns out to provide reasonable propos-als for balancing flight delays and controller interventions.In particular,it can reduce flight delays by 32.4% at a maxi-mum.The results imply that the proposed method can effectively reduce flight delays in busy airports and enhance the ro-bustness of aviation services.关键词
空中交通流量管理/进场排序/管制干预/Monte-Carlo 模拟/NSGA-II 算法Key words
air traffic flow management/aircraft sequencing/controller intervention/Monte-Carlo simulation/NSGA-II分类
航空航天引用本文复制引用
周雨凡,胡明华,张颖,高梦宇..基于 Monte-Carlo 模拟的进场排序不确定性研究[J].交通信息与安全,2016,34(4):22-28,7.基金项目
国家自然科学基金项目(71301074)资助 （71301074）
