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Optimizing Flight Departure Delay and Route Selection Under En Route Convective Weather

机译：航路对流天气下航班出发延误及航路选择的优化

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This paper presents an optimization model and a priority-based scheduling algorithm for managing air traffic flow in the United States. The models assign departure delays and pre-departure reroutes to aircraft whose trajectories are predicted to cross weather-impacted regions of the National Airspace System. It is ensured that the 4-dimensional trajectories of flights remain free of conflict with weather when the forecasts are deterministic. The optimization model and the scheduling algorithm are applied to solve a large-scale traffic flow management problem with realistic weather data and flight schedules. Experimental results indicate that allowing rerouting can reduce departure delays by nearly 67%, but it is associated with an increase in total airborne time due to longer routes flown by aircraft. The performances of the optimization model and the priority-based scheduling algorithm were compared. Delays obtained from the optimization model were 5% lower than those from the scheduling algorithm. However, the priority-based scheduling algorithm was about 5-times faster in generating solutions than the optimization model. This paper also discusses how airline rerouting preferences and some of the latest concepts in Collaborative Decision Making can be incorporated into the proposed models.
机译：本文提出了一种用于管理美国空中交通流量的优化模型和基于优先级的调度算法。这些模型为飞机的航迹预计会跨越国家空域系统的受天气影响的区域的飞机分配了起飞延迟和出发前的重新航线。当预测是确定性的时，可以确保飞行的4维轨迹与天气不会发生冲突。该优化模型和调度算法被用于解决具有实际天气数据和航班时刻表的大规模交通流管理问题。实验结果表明，允许改航可以将离港延误减少近67％，但由于飞机飞行的路线更长，这与总的空中飞行时间增加有关。比较了优化模型和基于优先级的调度算法的性能。从优化模型获得的延迟比从调度算法获得的延迟低5％。但是，基于优先级的调度算法在生成解决方案方面比优化模型快约5倍。本文还讨论了如何将航空公司改航的偏爱以及“协作决策”中的一些最新概念纳入所提出的模型中。

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