Ground Delay Program Planning Under Uncertainty in Airport Capacity

This paper presents an algorithm that can assign flight departure delays under probabilistic airport capacity. The algorithm dynamically adapts to weather forecasts by revising, if necessary, departure delays. The information required by the algorithm is considerably less than that required by existing stochastic dynamic optimization models. The proposed algorithm leverages state-of-the-art optimization techniques that have appeared in recent literature. It is a viable approach for planning ground delay programs at airports where probabilistic information on airport capacity are available. As a case study, the algorithm is applied to assigning departure delays to flights scheduled to arrive at San Francisco International Airport in the presence of uncertainty in the fog clearance time. The cumulative distribution function of fog clearance time was estimated from historical observations on 387 days in 2004-2007. When probabilistic weather forecasts were used to update the probabilities of fog clearance times, the expected delays obtained from the algorithm were reduced by approximately 7% compared to delays when the forecasts where not utilized. Experimental results also indicate that if the proposed algorithm is applied to assign ground delays to flights inbound at San Francisco International airport, overall delays could be reduced up to 20% compared to current level.