|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
Woodward, John R
|Title:||A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing|
|Citation:||Brownlee A, Woodward JR, Weiszer M & Chen J (2018) A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing. In: Proceedings of the Genetic and Evolutionary Computation Conference 2018. GECCO 2018: The 2018 conference on Genetic and Evolutionary Computation, Kyoto, Japan, 15.07.2018-19.07.2018. New York: ACM, pp. 1207-1213. https://doi.org/10.1145/3205455.3205558|
TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing
|Conference Name:||GECCO 2018: The 2018 conference on Genetic and Evolutionary Computation|
|Conference Dates:||2018-07-15 - 2018-07-19|
|Conference Location:||Kyoto, Japan|
|Abstract:||With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself. We apply a rolling window approach incorporating a genetic algorithm for permutations to this problem, for real-world scenarios at three busy airports. This is compared to an exhaustive approach over small rolling windows, and the conventional first-come-first-served ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches.|
|Status:||AM - Accepted Manuscript|
|Rights:||Publisher policy allows this work to be made available in this repository. Published in Proceedings of the Genetic and Evolutionary Computation Conference 2018 by ACM. The original publication is available at: https://doi.org/10.1145/3205455.3205558|
|rolling-window-ga-forrepository.pdf||Fulltext - Accepted Version||1.06 MB||Adobe PDF||View/Open|
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