A Chance-Constrained Optimization Approach for Air Traffic Flow Management Under Capacity Uncertainty

Airport capacity limitations remain a major problem for air traffic management, However, in the presence of capacity uncertainties, the ATFM operations may be impractical or ineffective when adopting the deterministic models since the latter assumes that airport capacity is known. In this paper, we propose a new approach based on Chance-Constrained Optimization Program (C-COP) taking into account airport capacity uncertainty to solve the Airport Network System Optimization (ANSO) problem. and a scenario optimization method for airport capacity in the presence of uncertainties is suggested to approximately solve the C-COP with a predetermined probabilistic confidence. Then, a mixed-integer programming model is developed based on the obtained deterministic capacity to minimize the total flight delay. The experiment findings proved that our model is more effective and reliable in handling airport capacity uncertainty. Our results also highlight the ability of the C-COP to solve ANSO problem when airports' capacities are uncertain.