Similarity based TOPSIS with linguistic-quantifier based aggregation using OWA

Abstract

In this paper we present similarity based TOPSIS with OWA operators. The motivation behind this new method is the fact that in many real world problems it is more important to consider the amount of criteria that a particular alternative is able to satisfy instead of simply concentrating on the importance of particular criteria. Here with OWA operators we can tackle this problem together with multi-criteria decision making method called TOPSIS by aggregating alternatives' similarities towards positive ideal solution and negative ideal solution and aggregating these similarities using OWA. The use of linguistic quantifiers represented by OWA weights generated by a selected RIM quantifier allows for the reflection of decision-maker's attitude to risk in the calculation of the similarities of the alternative with positive and negative ideal solutions.

Interval-valued semantic differential in multiple criteria and multi-expert evaluation context: possible benefits and application areas

“…The distances from the least desirable alternative (its evaluation) can also be reflected 'TOPSIS-style'. These distances can reflect also the weights of criteria, or even be based on OWA operators as proposed in the linguistic OWA-TOPSIS [60].…”

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

“…The distances from the least desirable alternative (its evaluation) can also be reflected 'TOPSIS-style'. These distances can reflect also the weights of criteria, or even be based on OWA operators as proposed in the linguistic OWA-TOPSIS [60].…”