Two-Stage Scalable Air Traffic Flow Management Model Under Uncertainty

Abstract

In order to efficiently balance the current and future air traffic demands with the system capacity, a proper Air Traffic Flow Management (ATFM) approach is required. The current focus of ATFM is generally on optimally utilizing the available airspace and airport capacities, while maintaining the required safety separation between aircraft. Yet, only a minor focus is given to the inherent uncertainty in the Air Transportation System (ATS), especially to its adverse effect on safety and day-to-day operations. To this end, we propose an ATFM framework scrutinizing the stochastic nature of ATS through a chance-constraint-based probabilistic approach. Moreover, anticipating the high volumes in air traffic in the future, we propose to split the model into two stages, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage transforms them into individual flight plans, enhancing scalability. The two models are formulated as an Integer Linear Programming (ILP) problem, and a Mixed Integer Linear Programming (MILP) problem at stages I and II, respectively. The NP-hard nature of the overall problem is minimized by transforming the problem into a Maximum Weighted Independent Set (MWIS) finding problem.