Choquet Integrals of Weighted Triangular Fuzzy Linguistic Information and Their Applications to Multiple Attribute Decision Making

Gao

2018

Int. J. Intell. Syst.

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396

247

The generalized Heronian mean and geometric Heronian mean operators provide two aggregation operators that consider the interdependent phenomena among the aggregated arguments. In this paper, the generalized Heronian mean operator and geometric Heronian mean operator under the q‐rung orthopair fuzzy sets is studied. First, the q‐rung orthopair fuzzy generalized Heronian mean (q‐ROFGHM) operator, q‐rung orthopair fuzzy geometric Heronian mean (q‐ROFGHM) operator, q‐rung orthopair fuzzy generalized weighted Heronian mean (q‐ROFGWHM) operator, and q‐rung orthopair fuzzy weighted geometric Heronian mean (q‐ROFWGHM) operator are proposed, and some of their desirable properties are investigated in detail. Furthermore, we extend these operators to q‐rung orthopair 2‐tuple linguistic sets (q‐RO2TLSs). Then, an approach to multiple attribute decision making based on q‐ROFGWHM (q‐ROFWGHM) operator is proposed. Finally, a practical example for enterprise resource planning system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Section: Q-rung Orthopair 2-tuple Linguistic Setsmentioning

confidence: 99%

Some q-rung orthopair fuzzy Heronian mean operators in multiple attribute decision making

Gao

2018

Int. J. Intell. Syst.

Self Cite

396

247

“…Compute the value of T ij i m ( = 1, 2,…, ; j n = 1, 2,…, ) based on Equations (49) Step 5´. Compute the score of γ ī ( = 1, 2, 3, 4) i , respectively: S γ (¯) = 0.5256 * 1 , S γ (¯) = 0.5520 * 2 , S γ (¯) = 0.5779 * 3 , S γ (¯) = 0.5338 * 4 .…”

Section: =12mentioning

confidence: 99%

“…Form last few decades, the IFS has been explored by many researchers and widely applied to many practical fields like clustering analysis, decision-making pattern recognition, and medical diagnosis. [3][4][5][6][7][8][9][10][11][12][13] Xu 14 proposed a series of aggregation operator's, namely, intuitionistic fuzzy weighted average (IFWA) operator, intuitionistic fuzzy ordered weighted average (IFOWA) operator, and intuitionistic fuzzy hybrid (IFHA) operator under IFS environment. Xu and Yager 10 developed intuitionistic fuzzy weighted geometric (IFWG) operator, intuitionistic fuzzy ordered weighted geometric (IFOWG) operator, and intuitionistic fuzzy hybrid (IFHG) operator.…”

Section: Introductionmentioning

confidence: 99%

“…IFS is characterized by membership degree and nonmembership degree and fulfill the condition that the sum of its membership and nonmembership degrees is less than or equal to 1. Form last few decades, the IFS has been explored by many researchers and widely applied to many practical fields like clustering analysis, decision‐making pattern recognition, and medical diagnosis . Xu proposed a series of aggregation operator’s, namely, intuitionistic fuzzy weighted average (IFWA) operator, intuitionistic fuzzy ordered weighted average (IFOWA) operator, and intuitionistic fuzzy hybrid (IFHA) operator under IFS environment.…”

Section: Introductionmentioning

confidence: 99%

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Multiattribute group decision-making based on Pythagorean fuzzy Einstein prioritized aggregation operators

2018

Pythagorean fuzzy set (PFS) is a powerful tool to deal with the imprecision and vagueness. Many aggregation operators have been proposed by many researchers based on PFSs. But the existing methods are under the hypothesis that the decision‐makers (DMs) and the attributes are at the same priority level. However, in real group decision‐making problems, the attribute and DMs may have different priority level. Therefore, in this paper, we introduce multiattribute group decision‐making (MAGDM) based on PFSs where there exists a prioritization relationship over the attributes and DMs. First we develop Pythagorean fuzzy Einstein prioritized weighted average operator and Pythagorean fuzzy Einstein prioritized weighted geometric operator. We study some of its desirable properties such as idempotency, boundary, and monotonicity in detail. Moreover we propose a MAGDM approach based on the developed operators under Pythagorean fuzzy environment. Finally, an illustrative example is provided to illustrate the practicality of the proposed approach.

“…Multiple attribute decision making problems under linguistic information processing environment is an interesting research topic having received more and more attention during the last several years. One of the well-known linguistic information processing models are the 2-tuple linguistic computational model (Beg & Rashid, 2015;Herrera, Herrera-Viedma 2000a, 2000bHerrera, Martı nez, & S anchez, 2005; Herrera and Mart ınez 2001b; Mart ınez-L opez, Rodr ıguez, Lin, Wei, Wang, & Zhao, 2014;Wu et al, 2015;Zavadskas & Turskis, 2011;Zavadskas, Turskis, & Kildien_ e, 2014;Zhang & Chu, 2009). Herrera and Mart ınez (2001a) show 2-tuple linguistic information processing manner can effectively avoid the loss and distortion of information.…”

Section: Introductionmentioning

confidence: 99%

Pythagorean 2-tuple linguistic power aggregation operators in multiple attribute decision making

Economic Research-Ekonomska Istraživanja

Gao

2020

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In this paper, we investigate the multiple attribute decision making problems with Pythagorean 2-tuple linguistic information. Then, we utilize power average and power geometric operations to develop some Pythagorean 2-tuple linguistic power aggregation operators: Pythagorean 2-tuple linguistic power weighted average (P2TLPWA) operator, Pythagorean 2-tuple linguistic power weighted geometric (P2TLPWG) operator, Pythagorean 2-tuple linguistic power ordered weighted average (P2TLPOWA) operator, Pythagorean 2-tuple linguistic power ordered weighted geometric (P2TLPOWG) operator, Pythagorean 2-tuple linguistic power hybrid average (P2TLPHA) operator and Pythagorean 2-tuple linguistic power hybrid geometric (P2TLPHG) operator. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the Pythagorean 2-tuple linguistic multiple attribute decision making problems. Finally, a practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.