The air traffic management (ATM) system is under considerable pressure, with congestion occurring almost daily, particularly during summer. Looking ahead, the system is expected to confront even greater complexity driven by sustained traffic growth, new airspace user categories, and more extreme weather events. In this context, efficiently managing limited airspace capacity while accounting for uncertainty in traffic demand has become critical. Dynamic airspace configuration provides a framework to maximize efficiency by adapting capacity to evolving traffic patterns, thereby reducing overflow, regulations, and delays. Given a predefined set of configurations, we aim to determine an optimal robust configuration plan that absorbs air traffic under demand uncertainty. To achieve this, we propose a robust optimization formulation that explicitly captures uncertainties in traffic demand and sector capacities, allowing solutions with adjustable protection levels against potential demand increases. To ensure computational efficiency, we design an algorithm that uses a constrained shortest-path procedure as its core component. We evaluate our approach at Madrid Area Control Center using available configurations and traffic data from August 2024. Results explore tradeoffs between minimizing overflow and increasing robustness, demonstrating that even moderate conservatism can substantially influence excess traffic and associated delays, underscoring the importance of computing optimal robust solutions.
Lui et al. (Sat,) studied this question.