Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27123
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation
Author(s): Brownlee, Alexander
Weiszer, Michal
Chen, Jun
Ravizza, Stefan
Woodward, John R
Burke, Edmund K
Contact Email: alexander.brownlee@stir.ac.uk
Keywords: Routing
Scheduling
Airport Operations
Optimization
Taxiing
Ground Movement
Uncertainty
Issue Date: Jul-2018
Citation: Brownlee A, Weiszer M, Chen J, Ravizza S, Woodward JR & Burke EK (2018) A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation, Transportation Research Part C: Emerging Technologies, 92, pp. 150-175. https://doi.org/10.1016/j.trc.2018.04.020.
SANDPIT: Integrating and Automating Airport Operations
EP/H004424/2
DAASE: Dynamic Adaptive Automated Software Engineering
EP/J017515/1
TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing
EP/N029577/1
Abstract: Allocating efficient routes to taxiing aircraft, known as the Ground Movement problem, is increasingly important as air traffic levels continue to increase. If taxiways cannot be reliably traversed quickly, aircraft can miss valuable assigned slots at the runway or can waste fuel waiting for other aircraft to clear. Efficient algorithms for this problem have been proposed, but little work has considered the uncertainties inherent in the domain. This paper proposes an adaptive Mamdani fuzzy rule based system to estimate taxi times and their uncertainties. Furthermore, the existing Quickest Path Problem with Time Windows (QPPTW) algorithm is adapted to use fuzzy taxi time estimates. Experiments with simulated taxi movements at Manchester Airport, the third-busiest in the UK, show the new approach produces routes that are more robust, reducing delays due to uncertain taxi times by 10-20% over the original QPPTW.
DOI Link: 10.1016/j.trc.2018.04.020
Rights: © 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

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