Integrated real-time airline control system using machine learning

使用机器学习的集成实时航空控制系统

• 批准号: 517414238
• 负责人: Professor Dr.-Ing. Hartmut Fricke
• 金额: --
• 依托单位: Institut für Luftfahrt und Logistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/517414238?language=en
• 关键词: Integrated; real; time; airline; control

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.

飞机周转（TA）涵盖了准备到达机场的下一个飞行部分所需的所有过程。该过程中涉及的员工，乘客和系统的互动，有时甚至不足的协调可能会导致干扰，这与反动网络效应一起是密集及时的飞行时间表中飞行延迟的主要原因。在光线上，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动延误以及错过的乘客，机组人员和飞机连接。适当的TA控制措施基于替代过程执行，更改的任务优先级以及精心选择的资源分配。但是，这些行动是由调度员决定的，他们目前基于不完整的信息以及较差甚至缺失的预测行动。此外，这些恢复措施的选择不是由乘客，飞机和机组恢复的整体做出的，当在文献中仅处理了两个计划步骤，它们已经在文献中被标记为“集成”优化。这些模型加上在机场和领空中有限的条件（有限的资源，容量）的组合是全新的。原因之一是该设置的常规求解器的运行时间过多。我们计划通过基于预先计算和类似的分类时间表中断的监督机器学习（ML）方法来克服这一障碍。最初的研究结果表明，基于ML的方法为琐碎的启发式方法提供了运行时的改进。基于先前发表的关于TA决策模型和单个车队旋转水平的研究结果，这些结果被整合到MI（N）LP优化模型中，并通过网络范围的措施（例如飞机交换或延迟管理）进行扩展。这使得在时间表的剩余时间范围内预测了反动延迟，并将有关乘客，飞机和机组人员的转移关系转换为本地评估功能。将实施选定的ML方法，以允许在飞行操作期间的中断与ML预测的中断中的中断进行比较。这将特定于问题的预期最佳度量作为限制经典优化的解决方案空间并支持融合中的常见求解器的其他约束。我们旨在确定适合基于培训的ML解决方案程序，以在机场的动态情况下，以延时的方式在机场的动态情况下进行决策过程的自动解决方案。