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dc.contributor.authorWeiszer, Michal
dc.contributor.authorChen, Jun
dc.contributor.authorStewart, Paul
dc.contributor.authorZhang, Xuejun
dc.date.accessioned2018-04-27T11:10:49Z
dc.date.available2018-04-27T11:10:49Z
dc.date.issued2018-04-21
dc.identifier.citationWeiszer, M. et al (2018) 'Preference-based evolutionary algorithm for airport surface operations', Transportation Research Part C: Emerging Technologies, Vol. 91 pp.296-316.en
dc.identifier.issn0968090X
dc.identifier.doi10.1016/j.trc.2018.04.008
dc.identifier.urihttp://hdl.handle.net/10545/622704
dc.description.abstractIn addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach.
dc.description.sponsorshipThis work is supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/H004424/1, EP/N029496/1 and EP/N029496/2.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0968090X18304650en
dc.rightsArchived with thanks to Transportation Research Part C: Emerging Technologiesen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAirportsen
dc.subjectRunway schedulingen
dc.subjectMultiobjective optimisationen
dc.titlePreference-based evolutionary algorithm for airport surface operations.en
dc.typeArticleen
dc.contributor.departmentQueen Mary University of Londonen
dc.contributor.departmentUniversity of Derbyen
dc.contributor.departmentBeihang Universityen
dc.contributor.departmentNational Key Laboratory of CNS/ATMen
dc.identifier.journalTransportation Research Part C: Emerging Technologiesen
refterms.dateFOA2019-02-28T17:05:37Z
html.description.abstractIn addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach.


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