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dc.contributor.authorDahooie, J. Heidary
dc.contributor.authorZavadskas, Edmundas Kazimieras
dc.contributor.authorFiroozfar, H.R.
dc.contributor.authorVanaki, Amir Salar
dc.contributor.authorMohammadi, N.
dc.contributor.authorBrauers, Willem Karel M
dc.date.accessioned2023-09-18T17:26:08Z
dc.date.available2023-09-18T17:26:08Z
dc.date.issued2019
dc.identifier.issn0952-1976
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/122901
dc.description.abstractMULTIMOORA (Multi-Objective Optimization on the basis of Ratio Analysis plus full multiplicative form) is a somewhat new multi criteria decision-making (MCDM) method which provides high efficiency and effectiveness in problem solving. To evaluate different alternatives and calculate their scores, it uses three major approaches; namely, ratio system (RS), reference point (RP), and full multiplicative form (FM). Based on the scores, alternatives are individually ranked in each approach. The obtained ranks are the basis for final ranking, which is determined under the rules of dominance theory. However, this method impose some limitations on the evaluation model such as the need for aggregation and final ranking based on each alternative's ranks already obtained by three different approaches (i.e., RS, RP, and FM) with no consideration to score differences, taking the same importance level for these approaches, the lack of attention to deviation of scores, the complexity of aggregation in the dominance theory and the need for multiple comparisons to obtain final rankings, and the probability of a circular reasoning with no distinction between alternatives included. In order to overcome shortcomings, we applied an objective weight determination method called CCSD (Correlation Coefficient and Standard Deviation) method to enhance the MULTIMOORA performance. In this regard, the scoring distance of every alternative is completely included in the aggregation. Using regression statistics, standard deviations and correlation coefficients; the dispersion in the set of scores is also computed in relation to three approaches of RS, RP, and FM. The application of unique weight for each approach will yield more realistic results. Further, it is no longer needed to use the dominance theory and the problems such as multiple comparisons and a circular reasoning are also eliminated. Finally, a real decision making problem is applied to select the proper technological forecasting method which illustrates the validity and practicality of the proposed approach.eng
dc.formatPDF
dc.format.extentp. 114-128
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScienceDirect
dc.relation.isreferencedbyINSPEC
dc.relation.isreferencedbyPubMed
dc.relation.isreferencedbyZentralblatt MATH (zbMATH)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.source.urihttps://www.sciencedirect.com/science/article/abs/pii/S0952197618302677?via%3Dihub
dc.source.urihttps://doi.org/10.1016/j.engappai.2018.12.008
dc.titleAn improved fuzzy MULTIMOORA approach for multi-criteria decision making based on objective weighting method (CCSD) and its application to technological forecasting method selection
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.references99
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionUniversity of Tehran
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionUniversity of Antwerp
dc.contributor.facultyStatybos fakultetas / Faculty of Civil Engineering
dc.contributor.departmentTvariosios statybos institutas / Institute of Sustainable Construction
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.researchfieldT 002 - Statybos inžinerija / Construction and engineering
dc.subject.vgtuprioritizedfieldsSD0404 - Statinių skaitmeninis modeliavimas ir tvarus gyvavimo ciklas / BIM and Sustainable lifecycle of the structures
dc.subject.ltspecializationsL102 - Energetika ir tvari aplinka / Energy and a sustainable environment
dc.subject.enCorrelation coefficient and standard deviation (CCSD)
dc.subject.enfuzzy MULTIMOORA
dc.subject.enmulti-attribute decision making
dc.subject.entechnological forecasting method
dcterms.sourcetitleEngineering applications of artificial intelligence
dc.description.volumevol. 79
dc.publisher.nameElsevier
dc.publisher.cityLondon
dc.identifier.doi2-s2.0-85060250008
dc.identifier.doi000459524300010
dc.identifier.doi10.1016/j.engappai.2018.12.008
dc.identifier.elaba34108477


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