RobustATM: Robust Optimization of ATM Planning Processes by Modelling of Uncertainty Impact

RobustATM: Robust Optimization of ATM Planning Processes by Modelling of Uncertainty Impact


Air Traffic Management (ATM) systems are driven by economic interests of the participating stakeholders and, therefore, are performance oriented. As possibilities of enlarging airport capacities are limited, one has to enhance the utilization of existing capacities to meet the continuous growth of traffic demand. The runway system is the main element that combines airside and groundside of the ATM System. Therefore, it is crucial for the performance of the whole ATM System that the traffic on a runway is planned efficiently. Such planning is one of the main challenges in ATM. Uncertainty, inaccuracy and non-determinism almost always lead to deviations from the actual plan or schedule. A typical strategy to deal with these changes is a regular re-computation or update of the schedule. These adjustments are performed in hindsight, i.e. after the actual change in the data occurred. The challenge is to incorporate uncertainty into the initial computation of the plans so that these plans are robust with respect to changes in the data, leading to a better utilization of resources, more stable plans and a more efficient support for ATM controllers and stakeholders. Incorporating uncertainty into the ATM planning procedures further makes the total ATM System more resilient, because the impact of disturbances and the propagation of this impact through the system is reduced.

In this research project, we investigate the problem of optimizing runway utilization focusing on the pre-tactical planning phase (i.e. we assume the actual planning time to be several hours, or at least 30 minutes, prior to actual arrival/departure times). We develop an appropriate mathematical optimization model for this particular planning phase and analyze the effect of disturbances on our solutions. Further, we incorporate uncertainties into the initial plan in order to retain its feasibility despite changes in the data. Therefore, we use techniques from Robust Optimization and Stochastic Optimization.


DLR, German Aerospace Center, Institute of Flight Guidance in Braunschweig.
This project is a SESAR WP E project funded by EUROCONTROL.


For further details about this project, please contact Manu Kapolke (manu.kapolke[at]

Friedrich-Alexander-Universität Erlangen-Nürnberg