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dc.contributor.authorSaracoglu, Burak Omer
dc.date.accessioned2016-04-07T13:20:56Z
dc.date.available2016-04-07T13:20:56Z
dc.date.issued2016-04
dc.identifier.citationSaracoglu, Burak Omer. A qualitative multi-attribute model for the selection of the private hydropower plant investments in Turkey: by foundation of the search results clustering engine (Carrot2), hydropower plant clustering, DEXi and DEXiTree. "Journal of Industrial Engineering and Management", Abril 2016, vol. 9, núm. 1, p. 152-178.
dc.identifier.issn2013-0953
dc.identifier.urihttp://hdl.handle.net/2117/85357
dc.description.abstractPurpose: The electricity demand in Turkey has been increasing for a while. Hydropower is one of the major electricity generation types to compensate this electricity demand in Turkey. Private investors (domestic and foreign) in the hydropower electricity generation sector have been looking for the most appropriate and satisfactory new private hydropower investment (PHPI) options and opportunities in Turkey. This study aims to present a qualitative multiattribute decision making (MADM) model, that is easy, straightforward, and fast for the selection of the most satisfactory reasonable PHPI options during the very early investment stages (data and information poorness on projects). Design/methodology/approach: The data and information of the PHPI options was gathered from the official records on the official websites. A wide and deep literature review was conducted for the MADM models and for the hydropower industry. The attributes of the model were identified, selected, clustered and evaluated by the expert decision maker (EDM) opinion and by help of an open source search results clustering engine (Carrot2) (helpful for also comprehension). The PHPI options were clustered according to their installed capacities main property to analyze the options in the most appropriate, decidable, informative, understandable and meaningful way. A simple clustering algorithm for the PHPI options was executed in the current study. A template model for the selection of the most satisfactory PHPI options was built in the DEXi (Decision EXpert for Education) and the DEXiTree software. Findings: The basic attributes for the selection of the PHPI options were presented and afterwards the aggregate attributes were defined by the bottom-up structuring for the early investment stages. The attributes were also analyzed by help of Carrot2. The most satisfactory PHPI options in Turkey in the big options data set were selected for each PHPI options cluster by the EDM evaluations in the DEXi. Originality/value: The recommended DEXi PHPI selection model by the search results clustering engine within a country wise case offered the possibility of easy, meaningful and satisfying continental or worldwide applications for the private investors and the international financial institutions such as the African Development Bank, or the World Bank was the main contribution.
dc.format.extent27 p.
dc.language.isoeng
dc.publisherOmniaScience
dc.rightsAttribution-NonCommercial 3.0 Unported
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses
dc.subject.lcshHydroelectric power-plants
dc.subject.lcshInvestments--Decision making
dc.subject.otherCarrot2
dc.subject.otherCluster
dc.subject.otherClustering
dc.subject.otherDecision
dc.subject.otherDEX
dc.subject.otherDEXi
dc.subject.otherDEXiTree
dc.subject.otherHydropower
dc.subject.otherInvestment
dc.subject.otherPrivate hydropower plant investments
dc.subject.otherQualitative scaled attributes
dc.subject.otherSearch results clustering engine
dc.subject.otherTurkey
dc.titleA qualitative multi-attribute model for the selection of the private hydropower plant investments in Turkey: by foundation of the search results clustering engine (Carrot2), hydropower plant clustering, DEXi and DEXiTree
dc.typeArticle
dc.subject.lemacCentrals hidroelèctriques
dc.subject.lemacInversions -- Presa de decisions
dc.identifier.dlB-28744-2008
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.citation.publicationNameJournal of Industrial Engineering and Management
local.citation.volume9
local.citation.number1
local.citation.startingPage152
local.citation.endingPage178


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