Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/1445
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dc.contributor.authorSremac, Sinišaen
dc.contributor.authorStević Ž.en
dc.contributor.authorPamučar D.en
dc.contributor.authorArsić M.en
dc.contributor.authorMatić, Bojanen
dc.date.accessioned2019-09-23T10:15:40Z-
dc.date.available2019-09-23T10:15:40Z-
dc.date.issued2018-08-01en
dc.identifier.urihttps://open.uns.ac.rs/handle/123456789/1445-
dc.description.abstract© 2018 by the authors. For companies active in various sectors, the implementation of transport services and other logistics activities has become one of the key factors of efficiency in the total supply chain. Logistics outsourcing is becoming more and more important, and there is an increasing number of third party logistics providers. In this paper, logistics providers were evaluated using the Rough SWARA (Step-Wise Weight Assessment Ratio Analysis) and Rough WASPAS (Weighted Aggregated Sum Product Assessment) models. The significance of the eight criteria on the basis of which evaluation was carried out was determined using the Rough SWARA method. In order to allow for a more precise consensus in group decision-making, the Rough Dombi aggregator was developed in order to determine the initial rough matrix of multi-criteria decision-making. A total of 10 logistics providers dealing with the transport of dangerous goods for chemical industry companies were evaluated using the RoughWASPAS approach. The obtained results demonstrate that the first logistics provider is also the best one, a conclusion confirmed by a sensitivity analysis comprised of three parts. In the first part, parameter r was altered through 10 scenarios in which only alternatives four and five change their ranks. In the second part of the sensitivity analysis, a calculation was performed using the following approaches: Rough SAW(Simple AdditiveWeighting), Rough EDAS (Evaluation Based on Distance from Average Solution), Rough MABAC (MultiAttributive Border Approximation Area Comparison), and Rough TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). They showed a high correlation of ranks determined by applying Spearman's correlation coefficient in the third part of the sensitivity analysis.en
dc.relation.ispartofSymmetryen
dc.titleEvaluation of a third-party logistics (3PL) provider using a rough SWARA-WASPAS model based on a new rough dombi aggregatoren
dc.typeJournal/Magazine Articleen
dc.identifier.doi10.3390/sym10080305en
dc.identifier.scopus2-s2.0-85052507755en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85052507755en
dc.relation.issue8en
dc.relation.volume10en
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptFakultet tehničkih nauka, Departman za saobraćaj-
crisitem.author.deptFakultet tehničkih nauka, Departman za građevinarstvo i geodeziju-
crisitem.author.parentorgFakultet tehničkih nauka-
crisitem.author.parentorgFakultet tehničkih nauka-
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