A Scenario Optimization Approach for Air Traffic Flow Management with Sector Capacity Uncertainty

In 2019, China's air traffic control service handled approximately 10.766 million flight movements, with an increase of 7.60% compared to 2018. However, this rapid growth has resulted in flight delays due to airspace capacity limitations, and air traffic control complications. Faced with the realities of airspace capacity limitations, Air Traffic Flow Management (ATFM) plays a central role in alleviating demand-capacity imbalances and subsequently reducing airspace congestion and flight delays. Yet, in the existence of airspace capacity uncertainty, the ATFM operations may be non-practical or ineffective when the deterministic models are adopted since the latter presumes that airspace capacity is known. In this paper, the Air Traffic Sector Network Flow Optimization (ATSNFO) problem is formulated into a Chance-Constrained Optimization Program (C-COP), and a scenario approach optimization method for airspace sector capacity in the presence of uncertainties is proposed to approximately solve the C-COP with a predetermined probabilistic confidence. Then, a mixed-integer programming model is developed based on the obtained deterministic sector capacity. Computation experiment results showed that our model is more efficient and reliable in dealing with uncertainty in ATFM problems. Our findings also highlight the capability of the scenario approach method for solving these problems.