An outranking approach with 2-tuple linguistic Fermatean fuzzy sets for multi-attribute group decision-making. (May 2023) - Akram, Muhammad; Bibi, Rabia; Deveci, Muhammet
Abstract: The 2-tuple linguistic Fermatean fuzzy set is an effective tool that combines the advantages of the reliable 2-tuple linguistic model with Fermatean fuzzy set. We aim to develop novel decision-making techniques based on 2TLFFS that can handle the situations in which linguistic labels are assigned to given data. The main objective of this study is to investigate ELECTRE II method for group decision-making in 2-tuple linguistic Fermatean fuzzy context and explain its implementation. The first phase employs suitable 2TLFF aggregation operator to assemble the expert's 2TLFF judgments on each alternative and set of criteria. The method then introduces three different sets (2TLFF concordance, 2TLFF indifferent and 2TLFF discordance sets) by pairwise comparison of alternatives. After that, the strong and weak outranking relations are developed through the comparison of concordance and discordance indices with threshold values (three concordance and two discordance levels). The strong and weak outranking graphs visually represent outranking relations that are ultimately investigated through a systematic iterative process that results in the alternatives' forward, reverse, and average rankings. A flowchart is developed to comprehend the algorithm of 2TLFF-ELECTRE II conveniently. A numerical example for the selection of optimal Extract, Transform and Load software for business intelligence describes the proposed decision-making technique. A thorough comparison with 2TLFF-CODAS and existing aggregation operators is carried out to illustrate the validity and supremacy of the proposed technique. Highlights: This article presents an outranking technique by using 2-tuple linguistic Fermatean fuzzy sets. The strong and weak outranking relations by comparing concordance and outranking relationships are developed. A numerical example of selecting the optimal ETL software for business intelligence by proposed method is solved. A comprehensive comparison of the proposed technique with existing decision-making methods is described.