Robust Schedules for Air Traffic Management

Robust Schedules for Air Traffic Management


Increasing air traffic and new procedures in air traffic management require a very efficient use of limited ATM resources (e.g., runway capacity, aircraft, fuel, passenger gates, arrival and departures routes, busses and other turnaround resources). It is impossible to create schedules for future use which never need to be adapted. Reasons are e.g., unexpected weather conditions, late passengers, and intended and unintended deviations from schedules. We tackle scheduling problems in ATM, like the planning of airplanes on runways. Therefore, the focus of the assigned task lies on modeling, understanding and controlling uncertainty in ATM problems. As described, there are several external factors causing unexpected effects. So it is important to concern with Resilience and Adaptation to continue having air transport and to be competitive to alternative transportation. Thus we have to accept these phenomena and have to incorporate uncertainty into the model.

ATM problems will be modeled by mixed integer programming and network theory. Since there is a high number of actors in ATM, e.g. stakeholders, agents and individuals, the interaction between each other increases consequently. This leads to models of complex systems. By incorporating robustness, we often get more complex structure.

A typical result would be a schedule that would be extremely robust to changes described by the uncertainty set. Even though changes in the data may occur that would not have been captured by the initial formulation, the solution is computed such that only minimal adjustments will be required to obtain a feasible plan with high utilization. We expect an improvement of tactical and pre-tactical controller support systems, which can also be used as what-if-tools. More air traffic can be handled while controller workload is still kept at an acceptable level to reduce the human error rate.


DLR, German Aerospace Center, Institute of Flight Guidance in Braunschweig.
This project is funded by the ComplexWorld Research Network of the SESAR WP E project.


For further details about this project, please contact Andreas Heidt (andreas.heidt[at]

Friedrich-Alexander-Universität Erlangen-Nürnberg