Abstract

Given central role of aviation as a transportation network and its remarkable economic impact, the air traffic demand is bound to increase. High traffic density in a given airspace region can cause safety issues and difficulties in monitoring tasks that can, in turn, result in flight delays. It is therefore crucial to efficiently organize the airspace structure to avoid under- and overloaded areas of the airspace.

We begin by describing how the airspace is structured, introducing the concept of sector and configuration and their capacity, and how to quantify the air traffic excess associated to a configuration. We will then introduce Dynamic Airspace Configuration as a method for optimally meeting the air traffic demand by adopting different configurations over time, thus determining a sequence of configurations (configuration plan); we impose that such a sequence also satisfies some operational restrictions that smooth the configuration dynamics, as to avoid, e.g., too frequent switching between configurations. After recalling the basic definitions and tools of (Integer) Linear Programming, we will present an Integer Linear Programming model that provides a configuration plan that minimizes the traffic excess for a given time frame, and a polyhedral study that explains its good computational performance. We conclude by showing the numerical results obtained by testing the model on five days of historical data (summer 2019) over the Madrid Area Control Center, with a focus on the comparison of different time discretizations and different restrictions on the configurations’ transitions.

The video of the seminar will appear shortly afterwards in this Mediaspace channel.

Seminario Dottorato