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dc.contributor.authorDeveci, M
dc.contributor.authorErdogan, N
dc.contributor.authorCali, U
dc.contributor.authorStekli, J
dc.contributor.authorZhong, S
dc.date.accessioned2021-06-03T11:03:20Z
dc.date.issued2021-06-03
dc.description.abstractThe technical, logistical, and ecological challenges associated with offshore wind development necessitate an extensive site selection analysis. Technical parameters such as wind resource, logistical concerns such as distance to shore, and ecological considerations such as fisheries all must be evaluated and weighted, in many cases with incomplete or uncertain data. Making such a critical decision with severe potential economic and ecologic consequences requires a strong decision-making approach to ultimately guide the site selection process. This paper proposes a type-2 neutrosophic number (T2NN) fuzzy based multi-criteria decision-making (MCDM) model for offshore wind farm (OWF) site selection. This approach combines the advantages of neutrosophic numbers sets, which can utilize uncertain and incomplete information, with a multi-attributive border approximation area comparison that provides formulation flexibility and easy calculation. Further, this study develops and integrates a techno-economic model for OWFs in the decision-making. A case study is performed to evaluate and rank five proposed OWF sites off the coast of New Jersey. To validate the proposed model, a comparison against three alternative T2NN fuzzy based models is performed. It is demonstrated that the implemented model yields the same ranking order as the alternative approaches. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order.en_GB
dc.identifier.citationVol. 103, article 104311en_GB
dc.identifier.doi10.1016/j.engappai.2021.104311
dc.identifier.urihttp://hdl.handle.net/10871/125923
dc.language.isoenen_GB
dc.publisherElsevier / International Federation of Automatic Control (IFAC)en_GB
dc.rightsCrown Copyright © 2021 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en_GB
dc.subjectDecision-makingen_GB
dc.subjectFuzzy setsen_GB
dc.subjectType-2 neutrosophic numberen_GB
dc.subjectSite selectionen_GB
dc.subjectOffshore winden_GB
dc.titleType-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USAen_GB
dc.typeArticleen_GB
dc.date.available2021-06-03T11:03:20Z
dc.descriptionThis is the final version. Available on open access from Elsevier via the DOI in this recorden_GB
dc.identifier.eissn0952-1976
dc.identifier.journalEngineering Applications of Artificial Intelligenceen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2021-05-19
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-06-03
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-06-03T10:33:10Z
refterms.versionFCDAM
refterms.dateFOA2021-06-03T11:03:37Z
refterms.panelBen_GB


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Crown Copyright © 2021 Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's licence is described as Crown Copyright © 2021 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).