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dc.date.accessioned2020-12-17T20:26:45Z
dc.date.available2020-12-17T20:26:45Z
dc.date.created2020-11-24T11:23:28Z
dc.date.issued2020
dc.identifier.citationMannino, Carlo Nakkerud, Andreas Sartor, Giorgio . Air Traffic Flow Management with Layered Workload Constraints. Computers & Operations Research. 2020, 127
dc.identifier.urihttp://hdl.handle.net/10852/81690
dc.description.abstractMany regions of the world are currently struggling with congested airspace, and Europe is no exception. Motivated by our collaboration with relevant European authorities and companies in the Single European Sky ATM Research (SESAR) initiative, we investigate novel mathematical models and algorithms for supporting the Air Traffic Flow Management in Europe. In particular, we consider the problem of optimally choosing new (delayed) departure times for a set of scheduled flights to prevent en-route congestion and high workload for air traffic controllers while minimizing the total delay. This congestion is a function of the number of flights in a certain sector of the airspace, which in turn determines the workload of the air traffic controller(s) assigned to that sector. We present a MIP model that accurately captures the current definition of workload, and extend it to overcome some of the drawbacks of the current definition. The resulting scheduling problem makes use of a novel formulation, Path&Cycle, which is alternative to the classic big-M or time-indexed formulations. We describe a solution algorithm based on delayed variable and constraint generation to substantially speed up the computation. We conclude by showing the great potential of this approach on randomly generated, realistic instances.
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleAir Traffic Flow Management with Layered Workload Constraints
dc.typeJournal article
dc.creator.authorMannino, Carlo
dc.creator.authorNakkerud, Andreas
dc.creator.authorSartor, Giorgio
cristin.unitcode185,15,13,25
cristin.unitnameStatistikk og Data Science
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1851518
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computers & Operations Research&rft.volume=127&rft.spage=&rft.date=2020
dc.identifier.jtitleComputers & Operations Research
dc.identifier.volume127
dc.identifier.doihttps://doi.org/10.1016/j.cor.2020.105159
dc.identifier.urnURN:NBN:no-84749
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn0305-0548
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/81690/2/1-s2.0-S0305054820302768-main.pdf
dc.type.versionPublishedVersion
cristin.articleid105159
dc.relation.projectNFR/267554
dc.relation.projectNFR/237718


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