One of the key challenges towards more automation in Air Traffic Control is the resolution of en-route conflicts. In this article we present a generic framework for the conflict resolution problem that clearly separates the trajectory and conflict models from the resolution. It is able to handle any kind of maneuver and detection models, though we propose our own realistic 3D maneuvers and conflict detection that takes into account uncertainties on the positioning of aircraft. Based on these models, realistic scenarios are built, for which potential conflicts are detected using an efficient GPU-based algorithm. The resulting instances of the conflict resolution problem are provided to the community as a public benchmark.

To efficiently solve this problem, we also introduce a generic framework for the cooperation of optimization algorithms. The framework benefits from the various optimization algorithms plugged to it by sharing relevant information among them, and is implemented as a distributed application for better performance. We illustrate its behavior on the conflict resolution problem with the cooperation between a Memetic Algorithm and an Integer Linear Program which consistently outperforms previous approaches by orders of magnitude. Instances with up to 60 aircraft are optimally solved within a few minutes using this framework, while each algorithm taken individually only provides sub-optimal solutions. This cooperative approach thus seems appropriate for application in a real-time context.

空中交通管制中实现更多自动化的主要挑战之一是解决航路冲突。在本文中，我们为冲突解决问题提供了一个通用框架，该框架将解决方案的轨迹和冲突模型清楚地分开了。尽管我们提出了自己的逼真的3D机动和冲突检测方法，但考虑到飞机位置的不确定性，它仍然可以处理任何类型的机动和检测模型。基于这些模型，构建了现实的场景，使用基于GPU的高效算法可以检测到潜在的冲突。由此产生的冲突解决问题实例将作为公共基准提供给社区。

为了有效解决此问题，我们还介绍了用于优化算法协作的通用框架。该框架通过在它们之间共享相关信息而受益于插入的各种优化算法，并被实现为分布式应用程序以提高性能。我们通过Memetic算法和Integer Linear Program之间的协作来说明其在冲突解决问题上的行为，该行为始终比以前的方法好几个数量级。使用此框架，可以在几分钟之内最佳解决多达60架飞机的实例，而单独采用的每种算法仅提供次优解决方案。因此，这种协作方法似乎适用于实时环境。