Weather-responsive optimization in air traffic flow management

Weather disruptions and shifting airspace demands in practice called for models that can better capture operational complexity and support more resilient decision-making. In this study, a stochastic optimization model is developed to address a region-based air traffic flow management (ATFM) optimization problem under weather uncertainty, incorporating temporally dynamic airspace configurations. Stochastic scenarios were constructed using historical meteorological aerodrome reports and a clustering-based approach. A scenario tree was generated and validated through stability analysis. The model was assessed over eight days using real ATFM data extracted from Demand Data Repository (DDR2) database of EUROCONTROL. The results indicate that the proposed approach reduced delays and yielded average cost savings of 43-71%. Notably, it substantially reduced air delays by 27% in the most likely scenario. These improvements also led to a 67% decrease in total early departure min and an 80% reduction in total early arrival min. In addition, the approach can aid decision-makers in identifying the most congested sectors–an essential step in formulating effective delay-reduction strategies.