A Scenario-Based Optimization Approach to Robust Estimation of Airport Capacity

Estimation of airport capacity plays a fundamental role in planning air traffic flow around the airport. Due to the impact of various dynamic factors on practical airport operation, e.g., the varying meteorological condition and changing fleet mix, airport capacity is characterized by uncertainties. The robustness of the existing iconic estimation approaches is challenged. This paper proposes a scenario-based optimization approach to robust estimation of airport capacity in the presence of the operational uncertainties. The capacity envelope identified through empirical analysis is associated with some probabilistic level and the estimation problem is then formulated as a chance-constrained optimization program approximately solved via scenario approach. Case study using real data set collected from Beijing Capital International Airport shows that the capacity envelope obtained by the proposed approach is more robust than two iconic approaches, i.e., proportion-based filtration approach and the quantile regression approach.