An information measure based extended VIKOR method in intuitionistic fuzzy valued neutrosophic value setting for multi-criteria group decision making

Ünver, Mehmet; Olgun, Murat; Garg, Harish

doi:10.24200/sci.2022.60039.6562

Cited by 3 publications

(1 citation statement)

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“…Qiyas et al [ 46 ] developed Fractional orthotriple fuzzy rough Hamacher aggregation operators and-their application on service quality of wireless network selection. Unver et al [ 52 ] presented an extended VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for solving group decision-making problems. The uncertainties given in the data were handled with the help of the intuitionistic fuzzy valued neutrosophic values (IFVNVs).…”

Section: Introductionmentioning

confidence: 99%

Some distance measures for triangular fuzzy numbers under technique for order of preference by similarity to ideal solution environment

Arora

Naithani

2023

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In our daily life, we always choose to access our decisions so that we can gain greatly from them based on our prior experiences. However, it might be challenging to reach the best decision in a fair amount of time due to the complex environment and lack of information about the system due to human error these days. Triangular fuzzy numbers are proving to be quite useful in many application fields because of their apparent flexibility in coping with the imprecision or uncertainty in the process of multi criteria decision making. A technique for order preference by similarity to ideal solution presents a solution for decision-makers that are usually multi attributes and involves a complex decision-making process. It is utilized due to its ability for considering both the qualitative and quantitative measures. The goal of this paper is to employ a technique for order preference by similarity to ideal solution‐based methodology to solve multicriteria group decision‐making problems proposed for triangular fuzzy environment. Proofs of axiomatic properties for distance measures is also discussed. Sensitivity analysis is used to improve the efficacy of the proposed measures. Comparison with the present measures is also performed. Our method requires fewer calculations and produces the improved results faster than previous methods.

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Section: Introductionmentioning

confidence: 99%