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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Main Authors: Jeremie Chevalier, Daniel Delahaye, Mohammed Sbihi, Pierre Marechal
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Aerospace
Subjects:
Online Access:https://www.mdpi.com/2226-4310/6/7/80
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spelling 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
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