Recent Advances in Business Analytics. Selected papers of the 2021 KNOWCON-NSAIS workshop on Business Analytics

Annals of Computer Science and Information Systems, Volume 29

Similarity based TOPSIS with linguistic-quantifier based aggregation using OWA


DOI: http://dx.doi.org/10.15439/2021B6

Citation: Recent Advances in Business Analytics. Selected papers of the 2021 KNOWCON-NSAIS workshop on Business Analytics, Jan Stoklasa, Pasi Luukka and Maria Ganzha (eds). ACSIS, Vol. 29, pages 4551 ()

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Abstract. In this paper we present similarity based TOPSISwith OWA operators. The motivation behind this new method isthe fact that in many real world problems it is more importantto consider the amount of criteria that a particular alternative isable to satisfy instead of simply concentrating on the importanceof particular criteria. Here with OWA operators we can tacklethis problem together with multi-criteria decision making methodcalled TOPSIS by aggregating alternatives' similarities towardspositive ideal solution and negative ideal solution and aggregatingthese similarities using OWA. The use of linguistic quantifiersrepresented by OWA weights generated by a selected RIMquantifier allows for the reflection of decision-maker's attitude torisk in the calculation of the similarities of the alternative withpositive and negative ideal solutions.


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