Evaluation of a Third-Party Logistics (3PL) Provider Using a Rough SWARA–WASPAS Model Based on a New Rough Dombi Aggregator

dc.citation.spage305
dc.citation.volume10
dc.contributor.authorSremac, Siniša
dc.contributor.authorStević, Željko
dc.contributor.authorPamučar, Dragan
dc.contributor.authorArsić, Miloš
dc.contributor.authorMatić, Bojan
dc.date.accessioned2023-07-20T07:42:10Z
dc.date.available2023-07-20T07:42:10Z
dc.date.issued2018
dc.description.abstractFor 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
dc.identifier.doi10.3390/sym10080305
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/500
dc.language.isoen
dc.publisherMDPI
dc.sourceSummetry
dc.subjectrough Dombi aggregator; rough SWARA; rough WASPAS; third-party logistics provider; dangerous goods
dc.titleEvaluation of a Third-Party Logistics (3PL) Provider Using a Rough SWARA–WASPAS Model Based on a New Rough Dombi Aggregator
dc.typeArticle
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