Mathematical programming formulations for robust airside terminal traffic flow optimisation problem

The robust traffic flow modelling approach offers a perspicacious and holistic surveillance for flight activities in a nearby terminal manoeuvring area. The real time flight information expedites the streaming control of terminal operations using computational intelligence. Hence, in order to reduce...

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Main Authors: Ng, Kam K.H., Chen, Chun-Hsien, Lee, C. K. M.
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160346
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1603462022-07-19T08:12:46Z Mathematical programming formulations for robust airside terminal traffic flow optimisation problem Ng, Kam K.H. Chen, Chun-Hsien Lee, C. K. M. School of Mechanical and Aerospace Engineering Engineering::Aeronautical engineering Decomposition Methods Robust Optimisation The robust traffic flow modelling approach offers a perspicacious and holistic surveillance for flight activities in a nearby terminal manoeuvring area. The real time flight information expedites the streaming control of terminal operations using computational intelligence. Hence, in order to reduce the adverse effect of severe uncertainty and the impact of delay propagation, the amplified disruption along with the terminal traffic flow network can be leveraged by using robust optimisation. The transit time from entry waypoint to actual landing time is uncertain since the true airspeed is affected by the wind direction and hazardous aviation weather in the terminal manoeuvring area. Robust optimisation for TTFP is to generate a solution against the uncertain outcomes, which implies that less effort by the ATC to perform re-scheduling is required. In addition, two decomposition methods are presented and proposed in this work. The computational performance of traditional Benders Decomposition will largely be affected by the infeasibility in the subsystem and resolution of infeasible solution in the second-stage optimisation problem resulting in a long iterative process. Therefore, we presented an enhanced Benders Decomposition method to tackle the infeasibility in the subsystem. As shown in the numerical experiments, the proposed method outperforms the traditional Benders Decomposition algorithm using Wilcoxon-signed ranks test and achieved a 58.52% improvement of solution quality in terms of solving one-hour flight traffic scenarios with an hour computation time limit. 2022-07-19T08:12:46Z 2022-07-19T08:12:46Z 2021 Journal Article Ng, K. K., Chen, C. & Lee, C. K. M. (2021). Mathematical programming formulations for robust airside terminal traffic flow optimisation problem. Computers and Industrial Engineering, 154, 107119-. https://dx.doi.org/10.1016/j.cie.2021.107119 0360-8352 https://hdl.handle.net/10356/160346 10.1016/j.cie.2021.107119 2-s2.0-85099778488 154 107119 en Computers and Industrial Engineering © 2021 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Aeronautical engineering
Decomposition Methods
Robust Optimisation
spellingShingle Engineering::Aeronautical engineering
Decomposition Methods
Robust Optimisation
Ng, Kam K.H.
Chen, Chun-Hsien
Lee, C. K. M.
Mathematical programming formulations for robust airside terminal traffic flow optimisation problem
description The robust traffic flow modelling approach offers a perspicacious and holistic surveillance for flight activities in a nearby terminal manoeuvring area. The real time flight information expedites the streaming control of terminal operations using computational intelligence. Hence, in order to reduce the adverse effect of severe uncertainty and the impact of delay propagation, the amplified disruption along with the terminal traffic flow network can be leveraged by using robust optimisation. The transit time from entry waypoint to actual landing time is uncertain since the true airspeed is affected by the wind direction and hazardous aviation weather in the terminal manoeuvring area. Robust optimisation for TTFP is to generate a solution against the uncertain outcomes, which implies that less effort by the ATC to perform re-scheduling is required. In addition, two decomposition methods are presented and proposed in this work. The computational performance of traditional Benders Decomposition will largely be affected by the infeasibility in the subsystem and resolution of infeasible solution in the second-stage optimisation problem resulting in a long iterative process. Therefore, we presented an enhanced Benders Decomposition method to tackle the infeasibility in the subsystem. As shown in the numerical experiments, the proposed method outperforms the traditional Benders Decomposition algorithm using Wilcoxon-signed ranks test and achieved a 58.52% improvement of solution quality in terms of solving one-hour flight traffic scenarios with an hour computation time limit.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Ng, Kam K.H.
Chen, Chun-Hsien
Lee, C. K. M.
format Article
author Ng, Kam K.H.
Chen, Chun-Hsien
Lee, C. K. M.
author_sort Ng, Kam K.H.
title Mathematical programming formulations for robust airside terminal traffic flow optimisation problem
title_short Mathematical programming formulations for robust airside terminal traffic flow optimisation problem
title_full Mathematical programming formulations for robust airside terminal traffic flow optimisation problem
title_fullStr Mathematical programming formulations for robust airside terminal traffic flow optimisation problem
title_full_unstemmed Mathematical programming formulations for robust airside terminal traffic flow optimisation problem
title_sort mathematical programming formulations for robust airside terminal traffic flow optimisation problem
publishDate 2022
url https://hdl.handle.net/10356/160346
_version_ 1739837439545966592