Slot Allocation for a Multi-Airport System Considering Slot Execution Uncertainty

Capacity–flow balance constitutes the primary challenge in strategic slot allocation. Both air traffic flow and airport flow are significantly influenced by departure/arrival times of flights. However, due to various uncontrollable factors such as flow control, delay propagation, and weather conditions, the actual departure/arrival times of flights inevitably deviate from their schedules. This reflects the inherent uncertainty in flight slot execution, which directly introduces uncertainty into capacity–flow analysis. In this paper, we develop an uncertainty slot allocation model for the multi-airport system (MAS), which incorporates slot execution deviation as an uncertainty factor with fix capacity restrictions formulated as chance constraints to balance robustness and optimality. To solve the model, we employ an equivalent model transformation approach and develop a scenario generation methodology. We applied our model to the MAS of Beijing–Tianjin for slot allocation. The results show that when the violation probability α∈[0,0.2] , the model achieved fully robust optimization. Even when α increases to 0.4, under all scenario combinations, at the selected fix, compared with the results of the deterministic model and original schedules, the number of peak flow time windows in the expected traffic statistics decreased by 84.6% and 75%, respectively, and the average maximum values of traffic in the maximum traffic statistics decreased by 31.1% and 33.5%, respectively. Furthermore, the incorporation of the chance constraint provides slot coordinators with flexible optimization solutions based on their acceptable risk levels.