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International audience; Humans still play a key role in air traffic control but their performances limit the capacity of the airspace and are responsible for delays. At the tactical level, even though air traffic controllers (ATCO) are trained for years, their performances are limited. In this article, we first isolated the tactical horizontal deconfliction task and explained its mathematical complexity. We observed through a simple experiment conducted on trainee and experienced ATCOs its complexity on random traffic in a part-task trainer displaying two to five aircraft trajectories at the same altitude. We compared performances of trainee ATCOs with experienced ATCOs using two different displays: a basic display showing information on aircraft positions and a dynamic visualization tool that represents the conflicting portions of aircraft trajectories and the evolution of the conflict zone when the user adds a maneuver to an aircraft. The tool allows the user to dynamically check the potential conflicting zones with the computer mouse before making a maneuver decision. Results showed that in easy situations (two aircraft), performance was similar with both displays and groups. However, as the complexity of the situations grows (from three to five aircraft), the dynamic visualization tool enables users to solve the conflicts more efficiently. Using the tool leads to fewer unsolved conflicts. Even if experienced ATCOs performed much better than trainee ATCOs on complex situations, they also performed much better with the conflict visualization tool than without on such situations.

International audience; Dynamic spatial ability is supposed to be involved in a critical process of air traffic controllers, namely conflict detection. The present paper aims at testing whether dynamic spatial ability improves with air traffic control training and/or experience. We designed a laboratory task to assess the performance in predicting if two moving disks would collide or not. We conducted a cross-sectional study with four groups of participants : ATCO trainees at the beginning (N=129), middle (N=80) or end of training (N=66) and experienced ATCOs (N=14). Results suggested on one hand that air traffic control training leads to a decrease in the number of extremely high proportions of undetected collisions from the middle of the training. On the other hand, air traffic control operational experience leads to a decrease in the number of extremely high proportions of falsely detected collisions.

International audience; The constant increase in air traffic, since the beginning of the commercial aviation, has led to problems of saturation on airports, approaching areas,or higher airspace.Whereas the aircraft are largely optimized and automated, the air traffic control is still essentially relying on human experience.The present case study details two problems of air traffic management (ATM) for which a genetic algorithm based solution has been proposed. Thefirst application deals with the en route conflict resolution problem. The second application deals with the traffic management problem in an airport platform.

International audience; La progression quasi-constante du trafic aérien depuis le début de l'aviation commerciale génère aujourd'hui des problèmes de saturation tant sur les plate- formes d'aéroport, que dans les zones d'approches ou dans l'espace aérien supérieur. Si les avions sont aujourd'hui largement optimisés et automatisés, on peut s'étonner que les tâches de contrôle soient restées pour la plupart artisanales, faisant appel à l'expérience humaine plus qu'à la puissance de calcul d'un ordinateur. Nous présentons dans ce document deux problèmes de gestion du trafic aérien pour lesquels un algorithme génétique permet de proposer des solutions. La première application se situe au niveau du trafic en route, et plus particulièrement du contrôle tactique. La deuxième application s'intéresse à la gestion du trafic sur une plate- forme aéroportuaire.

Due to air traffic growth and especially hubs development, major European airports can easily become bottlenecks in the global air transportation network. Therefore, accurate models of airport traffic prediction become more and more necessary for ground controllers. In this paper, a ground traffic simulation tool is proposed and applied to Roissy Charles De Gaulle airport. Two global optimization methods, using genetic algorithms at the airport, are developed to minimize taxiing time while respecting aircraft separation and runway capacities. In order to compare the efficiency of the different methods, simulations are carried out on a one day traffic sample, and ground delay is correlated to the traffic density at the airport.

Award : Prix math/info de l'académie des sciences de Toulouse 2015; Reliable global optimization is dedicated to finding a global minimum in the presence of rounding errors. The only approaches for achieving a numerical proof of optimality in global optimization are interval-based methods that interleave branching of the search-space and pruning of the subdomains that cannot contain an optimal solution. The exhaustive interval branch and bound methods have been widely studied since the 1960s and have benefittedfrom the development of refutation methods and filtering algorithms, stemming from the interval analysis and interval constraint programming communities. It is of the utmost importance: i) to compute sharp enclosures of the objective function and the constraints on a given subdomain; ii) to find a good approximation (an upper bound) of the globalminimum.State-of-the-art solvers are generally integrative methods, that is they embed local optimization algorithms to compute a good upper bound of the global minimum over each subspace. In this document, we propose a cooperativeframework in which interval methods cooperate with evolutionary algorithms. The latter are stochastic algorithms in which a population of individuals (candidate solutions) iteratively evolves in the search-space to reach satisfactory solutions. Evolutionary algorithms, endowed with operators that help individuals escape from local minima, are particularly suited for difficult problems on which traditional methods struggle to converge.Within our cooperative solver Charibde, the evolutionary algorithm and the interval- based algorithm run in parallel and exchange bounds, solutions and search-space via message passing. A strategy combining a geometric exploration heuristic and a domain reduction operator prevents premature convergence toward local minima and prevents theevolutionary algorithm from exploring suboptimal or unfeasible subspaces. A comparison of Charibde with state-of-the-art solvers based on interval analysis (GlobSol, IBBA, Ibex) on a benchmark of difficult problems shows that Charibde converges faster by an order of magnitude. New optimality results are provided for five multimodal problems, for which few solutions were available in the literature. We present an aeronautical application in which conflict solving between aircraft is modeled by an universally quantified constrained optimization problem, and solved by specific interval contractors. Finally, we certify the optimality of the putative solution to the Lennard-Jones cluster problem for five atoms, an open problem in molecular dynamics.; L’optimisation globale fiable est dédiée à la recherche d’un minimum global en présence d’erreurs d’arrondis. Les seules approches fournissant une preuve numérique d’optimalité sont des méthodes d’intervalles qui partitionnent l’espace de recherche et éliminent les sous-espaces qui ne peuvent contenir de solution optimale. Ces méthodes exhaustives, appelées branch and bound par intervalles, sont étudiées depuis les années 60 et ont récemment intégré des techniques de réfutation et de contraction, issues des communautés d’analyse par intervalles et de programmation par contraintes. Il est d’une importance cruciale de calculer i)un encadrement précis de la fonction objectif et des contraintes sur un sous-domaine; ii)une bonne approximation (un majorant) du minimum global. Les solveurs de pointe sont généralement des méthodes intégratives : ils invoquent sur chaque sous-domaine des algorithmes d’optimisation locale afin d’obtenir une bonne approximation du minimum global. Dans ce document, nous nous intéressons à un cadre coopératif combinant des méthodes d’intervalles et des algorithmes évolutionnaires. Ces derniers sont des algorithmes stochastiques faisant évoluer une population de solutions candidates (individus) dans l’espace de recherche de manière itérative, dans l’espoir de converger vers des solutions satisfaisantes. Les algorithmes évolutionnaires, dotés de mécanismes permettant de s’échapper des minima locaux, sont particulièrement adaptés à la résolution de problèmes difficiles pour lesquels les méthodes traditionnelles peinent à converger Au sein de notre solveur coopératif Charibde, l’algorithme évolutionnaire et l’algorithme sur intervalles exécutés en parallèle échangent bornes, solutions et espace de recherche par passage de messages. Une stratégie couplant une heuristique d’exploration géométrique et un opérateur de réduction de domaine empêche la convergence prématurée de la populationvers des minima locaux et évite à l’algorithme évolutionnaire d’explorer des sous-espaces sous-optimaux ou non réalisables. Une comparaison de Charibde avec des solveurs de pointe (GlobSol, IBBA, Ibex) sur une base de problèmes difficiles montre un gain de temps d’un ordre de grandeur. De nouveaux résultats optimaux sont fournis pour cinq problèmes multimodaux pour lesquels peu de solutions, même approchées, sont connues dans la littérature. Nous proposons une application aéronautique dans laquelle la résolution de conflits est modélisée par un problème d’optimisation sous contraintes universellement quantifiées, et résolue par des techniques d’intervalles spécifiques. Enfin, nous certifions l’optimalité de la meilleure solution connue pour le cluster de Lennard-Jones à cinq atomes, un problème ouvert en dynamique moléculaire.