Integrating and sequencing flows in Terminal Maneuvering Area by evolutionary algorithms

This paper presents a new approach for merging multiple aircraft flows in a Terminal Maneuvering Area (TMA). This work is motivated by the current overloaded airspace near large airports and the need of more efficient methods to help controllers. Some attempts to alleviate airspace congestion such as the minimum spacing requirements, negotiation of voluntary reductions in scheduled service, and the construction of additional runways at major airports have been done. Even though, more fundamental changes are needed to improve the use of available air capacity. Present research consists of a new approach to optimize a set of aircraft planned to land at a given airport; it is proposed to merge the incoming flows from different routes by mean of speed and path changes. Due to the high combinatoric induced by such a problem, a stochastic optimization algorithm has been developed in order to propose to each aircraft a new route and speed profile. Those changes aim to remove conflicts at merging points and to maintain separation of aircraft following the same route link according to their wake turbulence constraint. The optimization criteria is based on the minimum deviation from the initial path planning. This algorithm has been successfully applied to Gran Canaria airport in Spain with real traffic demand samples for which conflict free flow merging is produced smoothly with optimal runway feeding.