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）涵盖了到达机场的飞机为下一个航段做准备所需的所有过程。工作人员、乘客和系统之间的相互作用和有时缺乏协调，以及资源的有限性，可能导致中断，这与反应性网络效应一起，是时间密集的航班时刻表中航班延误的主要原因。在轻型方面，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，机组人员和飞机连接。适当的TA控制措施是基于可替代的过程执行、改变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，他们目前根据不完整的信息和糟糕甚至缺失的预测采取行动。此外，这些恢复措施的选择并不是由乘客、飞机和机组人员恢复整体地做出的，当这些规划步骤中只有两个被同时处理时，这些措施在文献中已经被标记为“集成”优化。在机场和空域的约束条件下（有限的资源、容量），这些模型加上TA的组合是全新的。其中一个原因是这种设置的传统求解器的过度运行时间。我们计划通过基于预先计算和类似分类的时间表中断的监督机器学习（ML）方法来克服这一障碍。初步的研究结果表明，基于ML的方法提供了运行时的改进琐碎的算法。基于先前发表的关于TA中的决策模型的研究结果，在单个机队轮换的水平上，这些被集成到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）来扩展。这使得能够预测整个时间表的剩余时间范围内的反应延迟，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函数。所选择的ML方法将被实施，以允许在飞行操作期间的中断与ML预测的中断进行比较，并使用相关的提前期来解决。这将特定问题的预期最优度量作为额外的约束来限制经典优化的解空间，并支持常见的求解器收敛。我们的目标是确定一种合适的基于监督训练的ML解决方案程序，用于以时间滚动的方式自动解决不同航空公司网络机场动态情况下的决策过程。