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A probabilistic model based optimization for aircraft scheduling in terminal area under uncertainty
Transportation Research Part C: Emerging Technologies ( IF 7.6 ) Pub Date : 2021-09-20 , DOI: 10.1016/j.trc.2021.103374
Ying Huo , Daniel Delahaye , Mohammed Sbihi

Air traffic management relies strongly on the accuracy of prediction, while uncertainties such as weather, navigation accuracy, pilot operations, etc. may weaken the performance of predictive tools and cause potential safety issues or reduced capacity. Among all controlled airspace, the Terminal Maneuvering Area (TMA) is one of the most complex areas in which flight safety can be easily affected by unpredictable disturbances. This paper addresses an aircraft scheduling problem under uncertainty with the aim of providing a robust schedule for arrival flights. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models - the deterministic model and a model that incorporates separation buffers - are presented as benchmarks. A meta-heuristic simulated annealing algorithm combined with a time decomposition sliding window is proposed for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented so as to evaluate the optimized solutions obtained from the three models. Statistical comparison among the results shows instability of the model that incorporates the separation buffers, in contrast, the proposed model has absolute advantages in both stability and the conflict absorbing ability when uncertainty arises.



中文翻译:

不确定性下航站区飞机调度的概率模型优化

空中交通管理高度依赖预测的准确性,而天气、导航准确性、飞行员操作等不确定性可能会削弱预测工具的性能并导致潜在的安全问题或容量下降。在所有管制空域中,终端机动区(TMA)是飞行安全最容易受到不可预测干扰影响的最复杂的区域之一。本文解决了不确定性下的飞机调度问题,目的是为到达航班提供可靠的时间表。不确定性量化和沿路线的传播是在轨迹模型中实现的,该模型将时间信息表述为随机变量。基于来自轨迹模型的预测时间信息,在预定义网络的航路点处执行冲突检测和解决。通过最小化预期的冲突次数,可以很好地分离连续运行的航班。除了提出的模型之外,还提出了另外两个模型——确定性模型和包含分离缓冲液的模型——作为基准。针对巴黎戴高乐 (CDG) 机场的案例研究,提出了一种结合时间分解滑动窗口的元启发式模拟退火算法。此外,实现了基于蒙特卡罗方法的仿真框架,以评估从三个模型中获得的优化解决方案。结果之间的统计比较显示了包含分离缓冲液的模型的不稳定性,

更新日期:2021-09-20
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