Distributionally Robust Ground Delay Programs with Learning-Driven Airport Capacity Predictions

TL;DR

This paper develops a distributionally robust optimization framework for ground delay programs that leverages deep learning to predict airport capacities and remains effective under distribution shifts.

Contribution

It introduces a novel integrated learning and optimization approach for robust ground delay planning amid uncertain airport capacities.

Findings

Deep learning model accurately forecasts capacity distributions.

Distributionally robust optimization outperforms stochastic methods under distribution shifts.

Framework enhances operational reliability of ground delay programs.

Abstract

Strategic Traffic Management Initiatives (TMIs) such as Ground Delay Programs (GDPs) play a crucial role in mitigating operational costs associated with demand-capacity imbalances. However, GDPs can only be planned (e.g., duration, delay assignments) with confidence if the future capacities at constrained resources (i.e., airports) are predictable. In reality, such future capacities are uncertain, and predictive models may provide forecasts that are vulnerable to errors and distribution shifts. Motivated by the goal of planning optimal GDPs that are \emph{distributionally robust} against airport capacity prediction errors, we study a fully integrated learning-driven optimization framework. We design a deep learning-based prediction model capable of forecasting arrival and departure capacity distributions across a network of airports. We then integrate the forecasts into a distributionally robust formulation of the multi-airport ground holding problem (\textsc{dr-MAGHP}). We show how \textsc{dr-MAGHP} can outperform stochastic optimization when distribution shifts occur, and conclude with future research directions to improve both the learning and optimization stages.