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Article Dans Une Revue Transportation research. Part C, Emerging technologies Année : 2020

Cooperation of combinatorial solvers for en-route conflict resolution


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.
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Dates et versions

hal-02733455 , version 1 (02-06-2020)



Ruixin Wang, Richard Alligier, Cyril Allignol, Nicolas Barnier, Nicolas Durand, et al.. Cooperation of combinatorial solvers for en-route conflict resolution. Transportation research. Part C, Emerging technologies, 2020, 114, pp.36-58. ⟨10.1016/j.trc.2020.01.004⟩. ⟨hal-02733455⟩
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