Dynamic capacity and variable runway configurations in airport slot allocation

The airport slot allocation procedure relies on a (fixed) capacity to allocate slots to airlines, which often leads to under/over utilization of slots, depending on how conservative the airport planners are. This demand and capacity imbalance over the peak period can best be addressed during the strategic planning phase by displacing flights from their originally allocated slots to neighboring ones with available capacity. The proposed Mixed Integer Programming (MIP) model addresses peak airport traffic with a leveling effect through minor adjustment of the demand with respect to a dynamic capacity derived from an analytical capacity model. The analytical capacity model provides a dynamic capacity estimation over the planning horizon based on a varying flight mix. Our approach also allows for exploration of possible runway configurations, arrival/departure priority, and operational modes (segregated/ mixed) to ensure that the higher levels of demand during the strategic planning phase does not lead to excessive delays on the day of operations. The MIP model lexicographically optimizes the flight slots, minimizing the number of displaced flights and total slot displacement for all flights, subject to scheduling, maximum acceptable slot displacement, capacity, and runway configuration constraints. We also investigate the benefit of the proposed model over fixed declared capacity models which do not account for operational mode changes. For the test data obtained for Singapore Changi airport, the proposed MIP model can handle 20% more flights over the current schedule with demand not exceeding estimated capacity in any slot, displacing less than 14% of flights with a maximum displacement of two slots (of fifteen minutes each). In addition, we explore the impact of segregated and mixed mode of runway operations on slot displacement. The proposed MIP model has the potential to be a strategic decision support tool for airport planners to allow them to manage future increase in demand with existing airport infrastructure and with minimum schedule adjustment.