Flow-based Flight Routing and Scheduling under Uncertainty

Abstract

To tackle the future air traffic demands and to enhance the safety of the Air Transportation System (ATS), a proper flight routing and scheduling scheme is required. This paper proposes an Air Traffic Flow Management (ATFM) model while considering the inherent uncertainties present in the ATS. The proposed model aims to reduce capacity violations and conflicts with the use of a probabilistic approach of chance constraint while minimizing adverse effects due to demand and capacity uncertainties. Further, the proposed approach uses the concept of flow-based modeling in which a set of flights are considered as a flow, to enlarge the problem space with the added feature of scalability. In the end, a flow decomposition strategy is used to obtain the individual flight information from the flow results. To the best of our knowledge, this is the first attempt to propose an ATFM model with a flow-based structure while considering both demand and capacity uncertainties. The optimization problem is formulated as an Integer Linear Programming (ILP) problem. The NP-hard nature of the overall problem is minimized by transforming the problem into a Maximum Weighted Independent Set (MWIS) finding problem.