  The airport ground holding problem seeks to minimize flight delay costs due
to reductions in the capacity of airports. However, the critical input of
future airport capacities is often difficult to predict, presenting a
challenging yet realistic setting. Even when capacity predictions provide a
distribution of possible capacity scenarios, such distributions may themselves
be uncertain (e.g., distribution shifts). To address the problem of designing
airport ground holding policies under distributional uncertainty, we formulate
and solve the airport ground holding problem using distributionally robust
optimization (DRO). We address the uncertainty in the airport capacity
distribution by defining ambiguity sets based on the Wasserstein distance
metric. We propose reformulations which integrate the ambiguity sets into the
airport ground holding problem structure, and discuss dicretization properties
of the proposed model. We discuss comparisons (via numerical experiments)
between ground holding policies and optimized costs derived through the
deterministic, stochastic, and distributionally robust airport ground holding
problems. Our experiments show that the DRO model outperforms the stochastic
models when there is a significant difference between the empirical airport
capacity distribution and the realized airport capacity distribution. We note
that DRO can be a valuable tool for decision-makers seeking to design airport
ground holding policies, particularly when the available data regarding future
airport capacities are highly uncertain.
