Scheduling Aircraft Landings with Ant Colony Optimisation

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

A problem faced by all major airports around the world is that of scheduling aircraft to land on available runways. A number of management and safety constraints must be satisfied when determining appropriate landing times for individual planes. This paper utilises a system based on biological principals, ant colony optimisation, in order to solve this problem. Some novel modifications are made to the basic algorithm and some competitive solutions for problems with up to 50 aircraft are generated.
Original languageEnglish
Title of host publicationIASTED International Conference on Artificial Intelligence and Soft Computing
EditorsH Leung
Place of PublicationBanff
Number of pages5
Publication statusPublished - Jul 2002

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Aircraft landing
Ant colony optimization
Scheduling
Aircraft
Landing
Airports

Cite this

Randall, M. (2002). Scheduling Aircraft Landings with Ant Colony Optimisation. In H. Leung (Ed.), IASTED International Conference on Artificial Intelligence and Soft Computing [357-020] Banff.
Randall, Marcus. / Scheduling Aircraft Landings with Ant Colony Optimisation. IASTED International Conference on Artificial Intelligence and Soft Computing. editor / H Leung. Banff, 2002.
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Randall, M 2002, Scheduling Aircraft Landings with Ant Colony Optimisation. in H Leung (ed.), IASTED International Conference on Artificial Intelligence and Soft Computing., 357-020, Banff.

Scheduling Aircraft Landings with Ant Colony Optimisation. / Randall, Marcus.

IASTED International Conference on Artificial Intelligence and Soft Computing. ed. / H Leung. Banff, 2002. 357-020.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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Randall M. Scheduling Aircraft Landings with Ant Colony Optimisation. In Leung H, editor, IASTED International Conference on Artificial Intelligence and Soft Computing. Banff. 2002. 357-020