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Ph.D de

Ph.D
Group : Learning and Optimization

Optimization and Uncertainty Handling in Air Traffic Management

Starts on 14/12/2010
Advisor : SCHOENAUER, Marc

Funding : Convention industrielle de formation par la recherche
Affiliation : Université Paris-Sud
Laboratory : LRI INRIA

Defended on 22/09/2014, committee :
Directeur de la thèse :
- Marc Schoenauer, DR INRIA, LRI/A&O

Co-encadrant:
- Pierre Savéant, docteur, Thales Research and Technology

Rapporteurs:
- Eric Feron, Professeur Dutton/Ducoffe of Aerospace Software Engineering GeorgiaTech

Examinateurs :
- Pierre Bessiere, DR CNRS au collège de France
- Nicolas Durand, Ingénieur en Chef des Ponts, des Eaux et des Forêts et Professeur à l'ENAC, pôle POM de la DSNA/DTI/R&D
- Xavier Gandibleux, professeur Université de Nantes, LINA/OPTI
- François Yvon, professeur Paris Sud, LIMSI/TLP

Superviseurs Thales :
- Areski Hadjaz
- Yann Le Bars

Research activities :

Abstract :
In this thesis, we investigate the issue of optimising the aircraft operators' demand with the airspace capacity by taking into account uncertainty in air traffic management.

In the first part of the work, we identify the main causes of uncertainty of the trajectory prediction (TP), the core component underlying automation in ATM systems.

We study the problem of online parameter-tuning of the TP during the climbing phase with the optimization algorithm CMA-ES.

The main conclusion, corroborated by other works in the literature, is that ground TP is not sufficiently accurate nowadays to support fully automated safety-critical applications.

Hence, with the current data sharing limitations, any centralized optimization system in Air Traffic Control should consider the human-in-the-loop factor, as well as other uncertainties.

Consequently, in the second part of the thesis, we develop models and algorithms from a network global perspective and we describe a generic uncertainty model that captures flight trajectory uncertainties and infer their impact on the occupancy count of the Air Traffic Control sectors.

This usual indicator quantifies coarsely the complexity managed by air traffic controllers in terms of number of flights.

In the third part of the thesis, we formulate a variant of the Air Traffic Flow and Capacity Management problem in the tactical phase for bridging the gap between the network manager and air traffic controllers.

The optimization problem consists in minimizing jointly the cost of delays and the cost of congestion while meeting sequencing constraints.

In order to cope with the high dimensionality of the problem, evolutionary multi-objective optimization algorithms are used with an indirect representation and some greedy schedulers to optimize flight plans.

An additional uncertainty model is added on top of the network model, allowing us to study the performances and the robustness of the proposed optimization algorithm when facing noisy context.

We validate our approach on real-world and artificially densified instances obtained from the Central Flow Management Unit in Europe.

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