Departure and Arrival Routes Optimization Near Large Airports
The bottleneck of today’s airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this pa...
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doaj-f49f2866c3e747f7afa1d022fa1b47342020-11-25T01:52:33ZengMDPI AGAerospace2226-43102019-07-01678010.3390/aerospace6070080aerospace6070080Departure and Arrival Routes Optimization Near Large AirportsJeremie Chevalier0Daniel Delahaye1Mohammed Sbihi2Pierre Marechal3Ecole Nationale de l’Aviation Civile (ENAC), Université Fédérale de Toulouse, 7 Avenue Edouard Belin, FR-31055 Toulouse CEDEX, FranceEcole Nationale de l’Aviation Civile (ENAC), Université Fédérale de Toulouse, 7 Avenue Edouard Belin, FR-31055 Toulouse CEDEX, FranceEcole Nationale de l’Aviation Civile (ENAC), Université Fédérale de Toulouse, 7 Avenue Edouard Belin, FR-31055 Toulouse CEDEX, FranceMathematical Institute of Toulouse, Université Toulouse III Paul Sabatier, F-31330 Toulouse, FranceThe bottleneck of today’s airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this paper, a solution for designing departure and arrival routes is proposed, which takes into account the runway configuration, the surroundings of the airport and operational constraints such as limited slopes or turn angles. The routes consist of two parts: a horizontal path in a graph constructed by sampling the TMA around the runway, to which is associated a cone of altitudes. The set of all routes is optimized by the Simulated Annealing metaheuristic. In the process and at each iteration, each route is computed by defining adequately the cost of the arcs in the graph and then searching a path on it. The costs are chosen so as to avoid zigzag behaviors as much as possible. Two tests were performed, one on an instance taken from the literature and the other on an artificial problem designed specifically to test this approach. The obtained results are satisfying with regard to the current state of air operations management and constraints.https://www.mdpi.com/2226-4310/6/7/80SID STAR designsimulated annealingglobal optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jeremie Chevalier Daniel Delahaye Mohammed Sbihi Pierre Marechal |
spellingShingle |
Jeremie Chevalier Daniel Delahaye Mohammed Sbihi Pierre Marechal Departure and Arrival Routes Optimization Near Large Airports Aerospace SID STAR design simulated annealing global optimization |
author_facet |
Jeremie Chevalier Daniel Delahaye Mohammed Sbihi Pierre Marechal |
author_sort |
Jeremie Chevalier |
title |
Departure and Arrival Routes Optimization Near Large Airports |
title_short |
Departure and Arrival Routes Optimization Near Large Airports |
title_full |
Departure and Arrival Routes Optimization Near Large Airports |
title_fullStr |
Departure and Arrival Routes Optimization Near Large Airports |
title_full_unstemmed |
Departure and Arrival Routes Optimization Near Large Airports |
title_sort |
departure and arrival routes optimization near large airports |
publisher |
MDPI AG |
series |
Aerospace |
issn |
2226-4310 |
publishDate |
2019-07-01 |
description |
The bottleneck of today’s airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this paper, a solution for designing departure and arrival routes is proposed, which takes into account the runway configuration, the surroundings of the airport and operational constraints such as limited slopes or turn angles. The routes consist of two parts: a horizontal path in a graph constructed by sampling the TMA around the runway, to which is associated a cone of altitudes. The set of all routes is optimized by the Simulated Annealing metaheuristic. In the process and at each iteration, each route is computed by defining adequately the cost of the arcs in the graph and then searching a path on it. The costs are chosen so as to avoid zigzag behaviors as much as possible. Two tests were performed, one on an instance taken from the literature and the other on an artificial problem designed specifically to test this approach. The obtained results are satisfying with regard to the current state of air operations management and constraints. |
topic |
SID STAR design simulated annealing global optimization |
url |
https://www.mdpi.com/2226-4310/6/7/80 |
work_keys_str_mv |
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