Hybrid Metaheuristic for Air Traffic Management with Uncertainty

  • S. ChaimatananEmail author
  • D. Delahaye
  • M. Mongeau
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)


To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations (TBO), that will require aircraft to follow an assigned 4D trajectory (time-constrained trajectory) with high precision. TBO involves separating aircraft via strategic (long-term) trajectory deconfliction rather than the currently-practicing tactical (short-term) conflict resolution. In this context, this chapter presents a strategic trajectory planning approach aiming at minimizing the number of conflicts between aircraft trajectories for a given day. The proposed methodology allocates an alternative departure time, a horizontal flight path, and a flight level to each aircraft at a nation-wide scale. In real-life situations, aircraft may arrive at a given position with some uncertainties on its curvilinear abscissa due to external events. To ensure robustness of the strategic trajectory plan, the aircraft arrival time to any given position will be represented here by a probabilistic distribution over its nominal assigned arrival time. The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the probability of potential conflicts between trajectories. A hybrid-metaheuristic optimization algorithm has been developed to solve this large-scale mixed-variable optimization problem. The algorithm is implemented and tested with real air traffic data taking into account uncertainty over the French airspace for which a conflict-free and robust 4D trajectory plan is produced


Hybrid metaheuristic Air traffic management Optimization under uncertainty 



This work has been supported by French National Research Agency (ANR) through JCJC program (project ATOMIC n ANR 12-JS02-009-01).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.ENAC, MAIAAUniv de Toulouse, IMTToulouseFrance

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