A Hybrid Metaheuristic Optimization Algorithm for Strategic Planning of 4D Aircraft Trajectories at the Continental Scale

Abstract

Global air-traffic demand is continuously increasing. To handle such a tremendous traffic volume while maintaining at least the same level of safety, a more efficient strategic trajectory planning is necessary. In this work, we present a strategic trajectory planning methodology which aims to minimize interaction between aircraft at the European-continent scale. In addition, we propose a preliminary study that takes into account uncertainties of aircraft positions in the horizontal plane. The proposed methodology separates aircraft by modifying their trajectories and departure times. This route/departure-time assignment problem is modeled as a mixed-integer optimization problem. Due to the very high combinatorics involved in the continent-scale context (involving more than 30,000 flights), we develop and implement a hybrid-metaheuristic optimization algorithm. In addition, we present a computationally-efficient interaction detection method for large trajectory sets. The proposed methodology is successfully implemented and tested on a full-day simulated air traffic over the European airspace, yielding to an interaction-free trajectory plan.