Some new Pythagorean fuzzy Choquet-Frank aggregation operators for multi-attribute decision making

Xing, Yuping; Zhang, Runtong; Wang, Jun; Zhu, Xiaomin

doi:10.1002/int.22025

Cited by 56 publications

(34 citation statements)

References 29 publications

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“…For example, to fairly treat membership and nonmembership degrees of PFSs, Ma et al [17] raised symmetry operations of PFSs and proposed a battery of Pythagorean fuzzy symmetric aggregation operators. Xing et al [18] put forward Pythagorean fuzzy Choquet integral aggregation operators based on Frank t-norm and t-conorm. To capture the interrelationship between aggregated Pythagorean fuzzy numbers (PFNs), Wei and Lu [19] put forward Pythagorean fuzzy Maclaurin symmetric mean operators.…”

Section: Introductionmentioning

confidence: 99%

Some q-Rung Dual Hesitant Fuzzy Heronian Mean Operators with Their Application to Multiple Attribute Group Decision-Making

et al. 2018

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The q-rung orthopair fuzzy sets (q-ROFSs), originated by Yager, are good tools to describe fuzziness in human cognitive processes. The basic elements of q-ROFSs are q-rung orthopair fuzzy numbers (q-ROFNs), which are constructed by membership and nonmembership degrees. As realistic decision-making is very complicated, decision makers (DMs) may be hesitant among several values when determining membership and nonmembership degrees. By incorporating dual hesitant fuzzy sets (DHFSs) into q-ROFSs, we propose a new technique to deal with uncertainty, called q-rung dual hesitant fuzzy sets (q-RDHFSs). Subsequently, we propose a family of q-rung dual hesitant fuzzy Heronian mean operators for q-RDHFSs. Further, the newly developed aggregation operators are utilized in multiple attribute group decision-making (MAGDM). We used the proposed method to solve a most suitable supplier selection problem to demonstrate its effectiveness and usefulness. The merits and advantages of the proposed method are highlighted via comparison with existing MAGDM methods. The main contribution of this paper is that a new method for MAGDM is proposed.

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Section: Introductionmentioning

confidence: 99%

Some q-Rung Dual Hesitant Fuzzy Heronian Mean Operators with Their Application to Multiple Attribute Group Decision-Making

et al. 2018

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“…Next, according to (11), calculate the parameter λ i A (λ j B ). Then, apply (12) to obtain the fuzzy measures on all combinations of the criteria.…”

Section: Matching Decision Making Based On Q-rung Orthopair Fuzzy Chomentioning

confidence: 99%

“…For the other members, utilize (8)-(10) to calculate the fuzzy measure on each criterion it uses (p = 1.5). The results are: Then, apply (12) to obtain the fuzzy measures on all combinations of the criteria. The detailed results are shown in Table 3.…”

Section: Decision Analysis With Our Proposed Approachmentioning

confidence: 99%

q ‐Rung orthopair fuzzy Choquet integral aggregation and its application in heterogeneous multicriteria two‐sided matching decision making

2019

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In the real decision making, q‐rung orthopair fuzzy sets ( q‐ROFSs) as a novel effective tool can depict and handle uncertain information in a broader perspective. Considering the interrelationships among the criteria, this paper extends Choquet integral to the q‐rung orthopair fuzzy environment and further investigates its application in multicriteria two‐sided matching decision making. To determine the fuzzy measures used in Choquet integral, we first define a pair of q‐rung orthopair fuzzy entropy and cross‐entropy. Then, by utilizing λ‐fuzzy measure theory, we propose an entropy‐based method to calculate the fuzzy measures upon criteria. Furthermore, we discuss q‐rung orthopair fuzzy Choquet integral operator and its properties. Thus, with the aid of q‐rung orthopair fuzzy Choquet integral, we consider the preference heterogeneity of the matching subjects and further explore the corresponding generalized model and approach for the two‐sided matching. Finally, a simulated example of loan market matching is given to illustrate the validity and applicability of our proposed approach.

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“…Wei et al [29], Geng et al [30], and Liu et al [31] investigated decision making problems with Pythagorean 2-tuple linguistic information and Pythagorean fuzzy uncertain linguistic information respectively. More contributions about PFSs in decision making can be found in literatures [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Some q-rung orthopair fuzzy Muirhead means with their application to multi-attribute group decision making

Wang

Zhang

Zhu

et al. 2019

IFS

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Recently proposed q-rung orthopair fuzzy set (q-ROFS) is a powerful and effective tool to describe fuzziness, uncertainty and vagueness. The prominent feature of q-ROFS is that the sum of membership and non-membership degrees is allowed to be greater than the one with the sum of qth power of the membership degree and qth power of the non-membership degree, which is equal to or less than 1. This characteristic makes the q-ROFS more powerful and useful than intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS). The aim of this paper is to develop some aggregation operators for fusing qrung orthopair fuzzy information. As the Muirhead mean (MM) is considered as a useful aggregation technology which can capture interrelationships among all aggregated arguments, we extend the MM to q-rung orthopair fuzzy environment and propose a family of q-rung orthopair fuzzy Muirhead mean operators. Moreover, we investigate some desirable properties and special cases of the proposed operators. Further, we apply the proposed operators to solve multi-attribute group decision making (MAGDM) problems. Finally, a numerical instance as well as some comparative analysis are provided to demonstrate the validity and superiorities of the proposed method.

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Some new Pythagorean fuzzy Choquet-Frank aggregation operators for multi-attribute decision making

Cited by 56 publications

References 29 publications

Some q-Rung Dual Hesitant Fuzzy Heronian Mean Operators with Their Application to Multiple Attribute Group Decision-Making

Some q-Rung Dual Hesitant Fuzzy Heronian Mean Operators with Their Application to Multiple Attribute Group Decision-Making

q ‐Rung orthopair fuzzy Choquet integral aggregation and its application in heterogeneous multicriteria two‐sided matching decision making

Some q-rung orthopair fuzzy Muirhead means with their application to multi-attribute group decision making

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