A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Nowadays, with the increasing traffic density of the European airspace, air traffic management includes the planning and monitoring of the capacity of the network as a way to facilitate the work of the air traffic controllers. This article presents the use of an evolutionary multiobjective algorithm for optimizing a schedule on the time of overflight of the waypoints. The evaluation function is defined as a probabilistic model, which reports the expected delays and the expected congestion giving the schedule. We believe that the robustness of such decision-support tool can only arise from a precise modeling of the uncertainty at a trajectory level and to propagate it to the sector level, allowing us to compute the probability of congestion. This paper gives the outline of the probabilistic model and the way it is used with an evolutionary algorithm in order to optimize the schedule. The proposed approach is tested against two artificial instances where one consists of 300 aircraft and 16 sectors.