Shubham Gupta

 

We present the first application of robust and adaptive optimization in the Air Traffic Flow Management (ATFM) problem. We introduce a weather-front based approach to model the uncertainty inherent in airspace capacity estimates resulting from the impact of a small number of weather fronts moving across the National Airspace (NAS). The key advantage of our uncertainty set construction is its low-dimensionality (uncertainty in only two parameters govern the overall uncertainty set for each airspace element). We formulate the resulting ATFM problem under capacity uncertainty within the robust and adaptive optimization framework and propose tractable solution methodologies.

We report empirical results from the proposed models on real-world flight schedules augmented with simulated weather fronts that illuminate the merits of our proposal. The key insights from our computational results are: i) the robust problem inherits all the attractive properties of the deterministic problem (e.g., strong integrality properties and fast computational times); and ii) the price of robustness and adaptability is typically small, thereby providing impetus to the practical application of our proposal.

This is joint work with Dimitris Bertsimas.