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dc.contributor.authorRiahi, V
dc.contributor.authorNewton, MAH
dc.contributor.authorSattar, A
dc.date.accessioned2019-06-09T01:31:42Z
dc.date.available2019-06-09T01:31:42Z
dc.date.issued2018
dc.identifier.isbn9783030039905
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-030-03991-2_32
dc.identifier.urihttp://hdl.handle.net/10072/383322
dc.description.abstractAircraft sequencing problem (ASP) is to schedule the operation times of departing and arriving aircraft such that their deviation from the desired operation times are minimised. There are two types of hard constraint which make this problem very challenging: time window constraint for the operation time of each aircraft, and minimum separation time between each pair of aircraft. ASP is known to be NP-Hard. Although some progress has been made in recent years in solving ASP, existing techniques still rely on generic algorithms that usually lack problem specific knowledge. This leads to either finding low quality solutions or scrambling with large-sized problems. In this work, we propose a constraint-guided local search algorithm that advances ASP search by injecting the specific knowledge of the problem into its different phases. In the intensification phase, we propose a greedy approach that gives more priorities to aircraft that are more problematic and create more delays. In the diversification phase, we employ a bounded-diversification technique that controls the new position of each selected aircraft and does not allow them to move very far away from their current positions. Computational results show that the proposed algorithm outperforms the existing state-of-the-art methods with considerable margin.
dc.description.peerreviewedYes
dc.publisherSpringer Nature
dc.publisher.placeSwitzerland
dc.relation.ispartofconferencename31st Australasian Joint Conference on Artificial Intelligence AI 2018
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofdatefrom2018-12-11
dc.relation.ispartofdateto2018-12-14
dc.relation.ispartoflocationWellington, New Zealand
dc.relation.ispartofpagefrom329
dc.relation.ispartofpageto341
dc.relation.ispartofvolume11320 LNAI
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleConstraint-guided local search for single mixed-operation runway
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionPost-print
gro.rights.copyright© 2018 Springer International Publishing AG. This is an electronic version of an article published in Lecture Notes In Computer Science (LNCS), volume 11320, AI 2018: AI 2018: Advances in Artificial Intelligence pp 329-341. Lecture Notes In Computer Science (LNCS) is available online at: http://link.springer.com// with the open URL of your article.
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gro.griffith.authorSattar, Abdul
gro.griffith.authorRiahi, Vahid
gro.griffith.authorNewton, MAHakim A.


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    Contains papers delivered by Griffith authors at national and international conferences.

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