Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3140
DC FieldValueLanguage
dc.contributor.authorDornemann, Jorin-
dc.contributor.authorRückert, Nicolas-
dc.contributor.authorFischer, Kathrin-
dc.contributor.authorTaraz, Anusch-
dc.date.accessioned2020-12-03T07:51:07Z-
dc.date.available2020-12-03T07:51:07Z-
dc.date.issued2020-09-23-
dc.identifier.citationHamburg International Conference of Logistics (HICL) 30: 337-381 (2020)de_DE
dc.identifier.isbn978-3-753123-47-9de_DE
dc.identifier.issn2365-5070de_DE
dc.identifier.urihttp://hdl.handle.net/11420/8045-
dc.description.abstractPurpose: The application of artificial intelligence (AI) has the potential to lead to huge progress in combination with Operations Research methods. In our study, we explore current approaches for the usage of AI methods in solving optimization prob-lems. The aim is to give an overview of recent advances and to investigate how they are adapted to maritime logistics. Methodology: A structured literature review is conducted and presented. The iden-tified papers and contributions are categorized and classified, and the content and results of some especially relevant contributions are summarized. Moreover, an eval-uation, identifying existing research gaps and giving an outlook on future research directions, is given. Findings: Besides an inflationary use of AI keywords in the area of optimization, there has been growing interest in using machine learning to automatically learn heuristics for optimization problems. Our research shows that those approaches mostly have not yet been adapted to maritime logistics problems. The gaps identi-fied provide the basis for developing learning models for maritime logistics in future research. Originality: Using methods of machine learning in the area of operations research is a promising and active research field with a wide range of applications. To review these recent advances from a maritime logistics' point of view is a novel approach which could lead to advantages in developing solutions for large-scale optimization problems in maritime logistics in future research and practice.en
dc.language.isoende_DE
dc.publisherepublide_DE
dc.rightsCC BY-SA 4.0de_DE
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/de_DE
dc.subjectLogisticsde_DE
dc.subjectIndustry 4.0de_DE
dc.subjectSupply Chain Managementde_DE
dc.subjectSustainabilityde_DE
dc.subjectCity Logisticsde_DE
dc.subjectMaritime Logisticsde_DE
dc.subjectData Sciencede_DE
dc.subject.ddc330: Wirtschaftde_DE
dc.subject.ddc380: Handel, Kommunikation, Verkehrde_DE
dc.titleArtificial intelligence and operations research in maritime logisticsde_DE
dc.typeinProceedingsde_DE
dc.identifier.doi10.15480/882.3140-
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0115422-
tuhh.oai.showtruede_DE
tuhh.abstract.englishPurpose: The application of artificial intelligence (AI) has the potential to lead to huge progress in combination with Operations Research methods. In our study, we explore current approaches for the usage of AI methods in solving optimization prob-lems. The aim is to give an overview of recent advances and to investigate how they are adapted to maritime logistics. Methodology: A structured literature review is conducted and presented. The iden-tified papers and contributions are categorized and classified, and the content and results of some especially relevant contributions are summarized. Moreover, an eval-uation, identifying existing research gaps and giving an outlook on future research directions, is given. Findings: Besides an inflationary use of AI keywords in the area of optimization, there has been growing interest in using machine learning to automatically learn heuristics for optimization problems. Our research shows that those approaches mostly have not yet been adapted to maritime logistics problems. The gaps identi-fied provide the basis for developing learning models for maritime logistics in future research. Originality: Using methods of machine learning in the area of operations research is a promising and active research field with a wide range of applications. To review these recent advances from a maritime logistics' point of view is a novel approach which could lead to advantages in developing solutions for large-scale optimization problems in maritime logistics in future research and practice.de_DE
tuhh.publisher.urlhttps://www.epubli.de/shop/buch/Data-Science-in-Maritime-and-City-Logistics-Wolfgang-Kersten-9783753123479/106048-
tuhh.publication.instituteMathematik E-10de_DE
tuhh.publication.instituteQuantitative Unternehmensforschung und Wirtschaftsinformatik W-4de_DE
tuhh.identifier.doi10.15480/882.3140-
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
tuhh.gvk.hasppnfalse-
tuhh.hasurnfalse-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.startpage337de_DE
tuhh.container.endpage381de_DE
dc.relation.conferenceHamburg International Conference of Logistics (HICL) 2020de_DE
dc.rights.nationallicensefalsede_DE
tuhh.relation.ispartofseriesProceedings of the Hamburg International Conference of Logistics (HICL)de_DE
tuhh.relation.ispartofseriesnumber30de_DE
local.contributorPerson.editorJahn, Carlos-
local.contributorPerson.editorKersten, Wolfgang-
local.contributorPerson.editorRingle, Christian M.-
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
datacite.resourceTypeConference Paper-
datacite.resourceTypeGeneralText-
item.grantfulltextopen-
item.contributorGNDJahn, Carlos-
item.contributorGNDKersten, Wolfgang-
item.contributorGNDRingle, Christian M.-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.creatorGNDDornemann, Jorin-
item.creatorGNDRückert, Nicolas-
item.creatorGNDFischer, Kathrin-
item.creatorGNDTaraz, Anusch-
item.openairetypeinProceedings-
item.tuhhseriesidProceedings of the Hamburg International Conference of Logistics (HICL)-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.creatorOrcidDornemann, Jorin-
item.creatorOrcidRückert, Nicolas-
item.creatorOrcidFischer, Kathrin-
item.creatorOrcidTaraz, Anusch-
item.languageiso639-1en-
item.seriesrefProceedings of the Hamburg International Conference of Logistics (HICL);30-
item.mappedtypeinProceedings-
item.contributorOrcidJahn, Carlos-
item.contributorOrcidKersten, Wolfgang-
item.contributorOrcidRingle, Christian M.-
crisitem.author.deptMathematik E-10-
crisitem.author.deptQuantitative Unternehmensforschung und Wirtschaftsinformatik W-4-
crisitem.author.deptQuantitative Unternehmensforschung und Wirtschaftsinformatik W-4-
crisitem.author.deptMathematik E-10-
crisitem.author.orcid0000-0003-3518-6039-
crisitem.author.orcid0000-0003-4178-7850-
crisitem.author.orcid0000-0002-4553-7499-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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