DC FieldValueLanguage
dc.contributor.authorLoukeris, Nikolaos-
dc.contributor.authorBezzina, Frank-
dc.contributor.authorMatsatsinis, Nikolaos-
dc.contributor.authorBekiros, Stelios-
dc.date.accessioned2024-04-22T09:44:20Z-
dc.date.available2024-04-22T09:44:20Z-
dc.date.issued2019-08-15-
dc.identifierscopus-85053034375-
dc.identifier.issn1572-9974-
dc.identifier.issn0927-7099-
dc.identifier.other85053034375-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2190-
dc.description.abstractOptimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this work we aim to reveal the most effective optimization methods, employing artificial intelligence approaches such as rough sets, neural networks, advanced classification methods as well as multi-criteria analysis under a comparative framework vis-à-vis their forecasting performance.en_US
dc.language.isoenen_US
dc.relation.ispartofComputational Economicsen_US
dc.subjectData miningen_US
dc.subjectDecision support systemsen_US
dc.subjectMulti-criteria decision analysisen_US
dc.subjectNeural networksen_US
dc.subjectPreference modelsen_US
dc.subjectRough setsen_US
dc.subjectShippingen_US
dc.titleCustomer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approachesen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10614-018-9842-5en_US
dc.identifier.scopus2-s2.0-85053034375-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume54en_US
dc.relation.issue2en_US
dc.identifier.spage647en_US
dc.identifier.epage667en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldEngineering and Technologyen_US
dc.journalsSubscriptionen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-1891-8245-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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