Integrated real-time airline control system using machine learning

飞机周转(TA)包括为抵达机场的飞机做好下一段飞行准备所需的所有流程。参与这一过程的工作人员、乘客和系统之间的互动，有时缺乏协调，以及有限的资源可用可能导致中断，再加上反动的网络效应，是密集时间航班时刻表中航班延误的主要原因。在光明的一面，TA提供了控制地面操作的潜力，从而支持准时性能，并避免反应性延误和错过乘客、机组人员和飞机的连接。适当的TA控制措施基于可选的流程执行、更改的任务优先级和精心选择的资源分配。然而，这些行动是由调度员决定的，他们目前的行动基于不完整的信息和糟糕的甚至错过的预测。此外，这些回收措施的选择并不是由乘客、飞机和机组人员回收的整体决定的，当只有两个规划步骤同时处理时，这些措施在文献中已经被贴上了“综合”优化的标签。在机场和空域的有限条件下(有限的资源和容量)，这些模型加上TA的组合是全新的。其中一个原因是此设置的传统求解器运行时间过长。我们计划通过基于预计算和类似分类时间表中断的监督机器学习(ML)方法来克服这一障碍。初步研究结果表明，基于ML的方法为琐碎的启发式算法提供了运行时改进。基于已发表的TA决策模型和单个机队轮换决策模型的研究成果，将这些模型集成到MI(N)LP优化模型中，并通过飞机调换或延误管理等网络范围的措施进行扩展。这使得能够在时间表的剩余时间范围内预测反应性延误，并将涉及乘客、飞机和机组人员的转移关系转换为本地评估函数。将实施选定的最大似然方法，以便将飞行操作期间的中断与最大似然预测的中断进行比较，并用相关的交货期解决。该算法将特定问题的期望最优度量固定为附加约束，以限制经典优化的解空间，并支持常见的解算器收敛。我们的目标是找出一种合适的基于监督训练的最大似然求解过程，用于机场动态情况下不同航空公司网络的决策过程的自动求解。

Aircraft turnaround (TA) covers all processes that are required to prepare the arriving aircraft at the airport for its next flight segment. The interaction and sometimes deficient coordination of staff, passengers and systems involved in the process as well as the limited availability of resources can lead to disruptions which, together with reactionary network effects, are the main causes of flight delays in densely timed flight schedules. On the light side, TA offers the potential to control ground operations, thus supporting on-time performance and avoiding reactionary delays and missed passenger, crew, and aircraft connections. The appropriate TA control measures are based on alternative process executions, changed task priorities, and a carefully selected allocation of resources. However, these actions are decided by dispatchers, who currently act based on incomplete information and poor or even missing predictions. Moreover, the choice of these recovery measures is not holistically made by passenger, aircraft, and crew recovery, which are labeled as "integrated" optimization in the literature already when only two of these planning steps are handled simultaneously. The combination of these models plus the TA under constrained conditions (limited resources, capacity) at the airport and in the airspace is entirely new. One reason for this is the excessive runtime of conventional solvers for this setup. We plan to overcome this barrier by supervised machine learning (ML) methods based on pre-computed and similar classified schedule disruptions. Initial research results show that ML-based approaches offer runtime improvement for trivial heuristics. Based on the previously published research results on decision models in TA and at the level of individual fleet rotations, these are integrated into an MI(N)LP optimization model and extended by network-wide measures such as aircraft swap or delay management. This enables the prediction of reactionary delays across the remaining time horizon of the schedule and transforms transfer relationships concerning passengers, aircraft, and crew into local evaluation functions. The selected ML method will be implemented to allow comparison of disruptions during flight operations with ML predicted disruptions being solved with relevant lead time. This fixes problem-specific expected optimal measures as additional constraints to restrict the solution space of classical optimization and supports common solvers in convergence. We aim to identify a suitable supervised training based ML solution procedure for the automated solution of decision processes within dynamic situations at airports for different airline networks and in a time-rolling manner.