Development of Heronian Mean-Based Aggregation Operators Under Interval-Valued Dual Hesitant q-Rung Orthopair Fuzzy Environments for Multicriteria Decision-Making

Deb, Nayana; Sarkar, Arun; Biswas, Animesh

doi:10.1007/978-981-19-1449-2_19

Cited by 1 publication

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, DH

q

‐ROFS exhibits more flexibility and usefulness over HFS, IFS, PFS, DHFS, dual hesitant Pythagorean fuzzy set (DHPFS) and

q

‐ROFS, which is presented in Table 1. It is important to mention that almost all the current DH

q

‐ROF information aggregation operations are developed based on Heronian mean (Deb et al, 2022; Xu et al, 2018), Hamacher (Sarkar et al, 2023; Wang, Wei, Wang, et al, 2019), Bonferroni mean (Sarkar & Biswas, 2021), Muirhead mean (Wang, Wei, Wei, & Wei, 2019) algebraic and geometric mean (Hussain et al, 2020), etc.…”

Section: Introductionmentioning

confidence: 99%

Schweizer‐Sklar operations based hybrid aggregation operator to dual hesitant q‐rung orthopair fuzzy set and its application on MCGDM

2023

Self Cite

View full text Add to dashboard Cite

Dual hesitant q-rung orthopair fuzzy (DHq-ROF) set appears as a powerful tool in compare to other variants of fuzzy sets to deal with uncertainties associated with available information in various real-life decision-making cases. In order to make DHq-ROF aggregation information process flexible, at first some operations viz., addition, multiplication, scalar multiplication, exponential laws based on Schweizer-Sklar class of t-conorms and t-norms are defined. Subsequently, using these operations, weighted average and geometric operators and ordered weighted average and geometric operators are introduced. But weighted average or geometric operators and ordered weighted average or geometric operators consider only the weight of the opinions and the weight of the ordered position of each given opinion respectively. To resolve weights of the arguments, hybrid aggregation operators viz., DHq-ROF Schweizer-Sklar hybrid averaging, DHq-ROF Schweizer-Sklar hybrid geometric operators are developed and their properties are discussed. Afterwards, a new method to deal with multicriteria group decision making problems under DHq-ROF environment is framed. To illustrate the proposed method a decision making problem related to investment company selection is considered and solved. To show the advantages of the proposed study, a comparative analysis among the developed and existing studies is discussed.dual hesitant fuzzy set, dual hesitant q-rung orthopair fuzzy set, group decision making, hybrid aggregation operators, Schweizer-Sklar t-conorms and t-norms | INTRODUCTIONMulticriteria decision making (MCDM) refers to the problem for sorting alternatives based on numerous criteria and choosing the best one. In actual decision-making circumstances, it is challenging to resolve MCDM problems having vague information due to the fuzziness of human cognition and the complexity of decision-making environments. Zadeh (1965) proposed the theory of fuzzy sets by introducing membership values only. Importing the concept of non-membership, Atanassov (1986) originated intuitionistic fuzzy (IF) sets (IFSs) and Yagar (2013Yagar ( , 2014 expanded the idea of IFS to create the Pythagorean fuzzy (PF) set (PFS), where the sum of the squares of membership and non-membership degrees is not greater than 1. Sometimes PFS fails to describe evaluation information in multicriteria group decision making (MCGDM problems). For example, PFS cannot consider 0:9,0:7 ð Þas a pair of membership and non-membership values. Because of 0:9 2 þ 0:7 2 > 1. To overcome this situation, Yagar (2017) defined q-rung orthopair fuzzy (q-ROF) set (q-ROFS), which is a generalized version of IFS and PFS satisfying the constraint 0 ≤ μ q þ ν q ≤ 1

show abstract

“…Thus, DH

q

‐ROFS exhibits more flexibility and usefulness over HFS, IFS, PFS, DHFS, dual hesitant Pythagorean fuzzy set (DHPFS) and

q

‐ROFS, which is presented in Table 1. It is important to mention that almost all the current DH

q

Section: Introductionmentioning

confidence: 99%

Schweizer‐Sklar operations based hybrid aggregation operator to dual hesitant q‐rung orthopair fuzzy set and its application on MCGDM

2023

Self Cite

View full text Add to dashboard Cite

show abstract

Development of Heronian Mean-Based Aggregation Operators Under Interval-Valued Dual Hesitant q-Rung Orthopair Fuzzy Environments for Multicriteria Decision-Making

Cited by 1 publication

References 55 publications

Schweizer‐Sklar operations based hybrid aggregation operator to dual hesitant q‐rung orthopair fuzzy set and its application on MCGDM

Schweizer‐Sklar operations based hybrid aggregation operator to dual hesitant q‐rung orthopair fuzzy set and its application on MCGDM

Contact Info

Product

Resources

About