Optimisation of operation flight plan using genetic algorithm

Commercial airline optimisation of operation flight plan requires a list of waypoints or Navaids and their corresponding flight levels in which the aircraft should fly. Although Dijkstra’s Algorithm (DA), which is currently used by Flight Focus Pte Ltd, can find the global optimal route, it is too t...

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Main Author: Loh, Kai Leong.
Other Authors: School of Mechanical and Aerospace Engineering
Format: Final Year Project
Language:English
Published: 2011
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Online Access:http://hdl.handle.net/10356/45750
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-457502023-03-04T18:51:35Z Optimisation of operation flight plan using genetic algorithm Loh, Kai Leong. School of Mechanical and Aerospace Engineering Flight Focus Pte Ltd Tegoeh Tjahjowidodo DRNTU::Engineering::Aeronautical engineering::Air navigation Commercial airline optimisation of operation flight plan requires a list of waypoints or Navaids and their corresponding flight levels in which the aircraft should fly. Although Dijkstra’s Algorithm (DA), which is currently used by Flight Focus Pte Ltd, can find the global optimal route, it is too time-consuming to perform 3-Dimensional (3D) optimisation. Current optimisation is achieved by performing horizontal (longitude and latitude) optimisation first, then using the results for vertical (flight level) optimisation. Genetic Algorithm (GA) presents itself as a potential candidate for coupled, 3D optimisation and the purpose of this project is to proof the feasibility. The scope is narrowed to horizontal optimisation where traditional GA is modified to allow such application. Modified GA represented the waypoints using genes in variable length chromosomes and performed genetic operations using multiple point crossover and mutation. Roulette wheel selection method and elitism were used in the algorithm as well. Extensive tests on the modified GA proved that it was functional and converged to an optimum that was less than 5%, average of around 1% and mode less than 0.25% with respect to global optimum values. However, results also indicated that biasness in route creation might have caused occurrences of local optimal solutions. Despite the possible drawbacks from route creation, Flight Focus Pte Ltd finds the study conclusive in proving the concept and supports future studies. Bachelor of Engineering (Aerospace Engineering) 2011-06-16T09:17:36Z 2011-06-16T09:17:36Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/45750 en Nanyang Technological University 118 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Aeronautical engineering::Air navigation
spellingShingle DRNTU::Engineering::Aeronautical engineering::Air navigation
Loh, Kai Leong.
Optimisation of operation flight plan using genetic algorithm
description Commercial airline optimisation of operation flight plan requires a list of waypoints or Navaids and their corresponding flight levels in which the aircraft should fly. Although Dijkstra’s Algorithm (DA), which is currently used by Flight Focus Pte Ltd, can find the global optimal route, it is too time-consuming to perform 3-Dimensional (3D) optimisation. Current optimisation is achieved by performing horizontal (longitude and latitude) optimisation first, then using the results for vertical (flight level) optimisation. Genetic Algorithm (GA) presents itself as a potential candidate for coupled, 3D optimisation and the purpose of this project is to proof the feasibility. The scope is narrowed to horizontal optimisation where traditional GA is modified to allow such application. Modified GA represented the waypoints using genes in variable length chromosomes and performed genetic operations using multiple point crossover and mutation. Roulette wheel selection method and elitism were used in the algorithm as well. Extensive tests on the modified GA proved that it was functional and converged to an optimum that was less than 5%, average of around 1% and mode less than 0.25% with respect to global optimum values. However, results also indicated that biasness in route creation might have caused occurrences of local optimal solutions. Despite the possible drawbacks from route creation, Flight Focus Pte Ltd finds the study conclusive in proving the concept and supports future studies.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Loh, Kai Leong.
format Final Year Project
author Loh, Kai Leong.
author_sort Loh, Kai Leong.
title Optimisation of operation flight plan using genetic algorithm
title_short Optimisation of operation flight plan using genetic algorithm
title_full Optimisation of operation flight plan using genetic algorithm
title_fullStr Optimisation of operation flight plan using genetic algorithm
title_full_unstemmed Optimisation of operation flight plan using genetic algorithm
title_sort optimisation of operation flight plan using genetic algorithm
publishDate 2011
url http://hdl.handle.net/10356/45750
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