Dynamic Airspace Configuration Under Uncertainty

De Giovanni, Luigi and Galeazzo, Martina and Lui, Go Nam and Lulli, Guglielmo (2025) Dynamic Airspace Configuration Under Uncertainty. In: 2025 INTERNATIONAL CONFERENCE on OPTIMIZATION and DECISION SCIENCE, 2025-09-01 - 2025-09-04.

Abstract

Air traffic control is crucial in air transportation and faces growing challenges driven by factors factors like increasing air traffic demand, integration of new airspace users, adverse weather conditions. In this context, we consider the airspace as partitioned into sectors. A sector is an airspace region with a defined capacity, the maximum traffic that can be safely monitored within it. A configuration is a specific partition of the airspace into sectors. Dynamic Airspace Configuration improves efficiency by adapting the configuration over time, to align capacity with demand patterns that vary in space and time [1,2]. The goal is to minimize the traffic exceeding sector capacities, thus reducing the need for regulations and delays. We consider a predetermined set of configurations and aim to determine an optimal sequence of configurations that effectively absorbs traffic while satisfying operational constraints on the configuration dynamics to guarantee smooth transitions. Moreover, we consider the problem under traffic demand uncertainty and seek robust solutions that minimize worst-case traffic overflow, with varying degrees of conservatism [3]. We propose Integer Linear Programming models that extend previous formulations [2] to account for uncertainty and provide optimal solutions that are robust against possible traffic demand variations. We devise a cutting-planes approach to deal with different uncertainty sets. In the case this set is a box, a more efficient graph-based formulation is solved by constrained shortest path algorithms. We evaluate the approach on synthetic and realistic instances, using available conf igurations and historical traffic data. Our computational study explores the trade-off between minimizing traffic overflow and robustness.