Multiple attribute decision making problem using GRA method with incomplete weight information based on picture hesitant fuzzy setting

Ullah, Waheed; Ibrar, Muhammad; Khan, Asghar; Khan, M. Salim

doi:10.1002/int.22324

Cited by 36 publications

(12 citation statements)

References 37 publications

(40 reference statements)

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“…Recently, in a paper [42], of Aydemir & Gunduz discussed TOPSIS method in terms of Dombi aggregation operators based on FF-sets and gave a complete overview of FF-sets in the frame-work of Dombi operations. In [43], the authors have extended FF-set into Hamacher operations and investigated the basic properties of FF-sets in Hamacher operations. Some practical examples of real world scenario were discussed as practical example for the validation of the theory.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach to MADM problems using Fermatean fuzzy Hamacher prioritized aggregation operators

Jan

Khan

et al. 2021

Soft Comput

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A generalized form of union and intersection on FFS can be formulated from a generalized t-norm (TN) and t-conorm (TCN). Hamacher operations such as Hamacher product and Hamacher sum, are good alternatives to produce such product and sum. The Hamacher operations can generate more ‡exible and more accurate results in decision making process due to the working parameter involved in these operations. The intuitionistic fuzzy set, brifely as; IFS and its extension involving Pythagorean fuzzy set (PFS) and Fermatean fuzzy set (FFS), are all e¤ective tools to express uncertain and incomplete cognitive information with membership, nonmembership and hesitancy degrees. The Fermatean fuzzy set (FF-set) carries out uncertain and imprecise information smartly in exercising decision-making than IFS and PFS. By adjusting the prioritization of attributes in FF-environment, in this course of this article, we …rst device new operations on FF information using prioritized attributes and by employing HTN and HTCN, we discuss the basic operations. Induced by the Hamacher operations and FF-set, we propose FF Hamacher arithmetic and also geometric aggregation operators (AOs). In the …rst section, we introduce the concepts of an FF Hamacher prioritized AO, and FF Hamacher prioritized weighted AO. In the second part, we develop FF Hamacher prioritized geometric operator (GO), and FF Hamacher prioritized weighted GO. We study essential properties and a few special cases of our newly proposed operators. Then, we make use of these proposed operators in developing tools which are key factors in solving the FF multi-attribute decision-making situations with prioritization. The university selection phenomena is considered as a direct application for analysis and to demonstrate the practicality and e¢ cacy of our proposed model. The working parameter considered in these AOs is analyzed in di¤erent existing and proposed AOs. Further, comparison analysis is conducted for the authenticity of proposed & existing operators.

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Section: Introductionmentioning

confidence: 99%

“…Some practical examples of real world scenario were discussed as practical example for the validation of the theory. Keeping in view the work on FF-set, we intend to extend the work of [43] The remaining parts of the paper are organized in the following lines.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to MADM problems using Fermatean fuzzy Hamacher prioritized aggregation operators

Jan

Khan

et al. 2021

Soft Comput

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“…Jana and Pal 27 proposed a hybrid method GRA in multiattribute decision making. A model of hydrogen production using GRA is proposed by Li et al 28 Estimation for Li‐ion battery health with GRA approach has been proposed by Li et al 29 GRA is used for pattern recognition by Sun et al, 30 Ullah et al 31 developed GRA approach in picture fuzzy environment, Wu et al 32 developed GRA method in product design, Yang and Chen 33 proposed a combined approach with GRA and AHP. A fuzzy multiattribute decision‐making method in the GRA framework has been proposed by Yazdani et al 34 Saaty 65 developed the commanding work of AHP in the year 1970 for project prioritization, and it was proposed on the information domain, which is crisp in nature.…”

Section: Introductionmentioning

confidence: 99%

Portfolio selection as a multicriteria group decision making in Pythagorean fuzzy environment with GRA and FAHP framework

2021

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The popularity of mutual funds, which are necessarily portfolios, has been drawing more attention from the people of India over the last three decades. In a mutual fund, one can invest his or her money in the securities of different sectors traded mainly in the stock exchange markets to get expected return bearing tolerable risks. A mutual fund other than the passive mutual funds is directed by an active fund manager. The performance of a mutual fund depends on the shares and securities of different companies it contains and the fund manager's performance also. Risk and return can be measured based on different criteria. Before investment, one should select such a mutual fund that can fulfill his or her anticipation as much as possible within the risk–return skeleton. Therefore the selection of a mutual fund is rigorously a multicriteria decision making. This paper has considered five open‐ended, large‐cap, direct, suspended sales mutual funds for the research work. In the first stage, we have shortlisted the more crucial criteria comparatively from a list of criteria with the help of the Fuzzy Analytic Hierarchy Process. In the second stage, we have applied the Pythagorean fuzzy Gray Relation Analysis approach to determine the weights of shortlisted criteria as well as the rank of those mutual funds. Finally, a comparison has been drawn between the present model and the Pythagorean fuzzy Interactive Multicriteria Decision‐Making model, to show the effectiveness of the present model.

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“…Zeb et al [24] presented the notion of extended Pythagorean fuzzy set and applied this concept to solve preference risk decision-making problem. Ullah et al [25] initiated the concept of GRA method using picture hesitant fuzzy numbers with incomplete weight information. Some scholars are working in the field of PFS theory and introduced different type of DM approaches (Wang et al [26] and Wang et al [27]).…”

Section: Introductionmentioning

confidence: 99%

Extended TOPSIS Method for Supplier Selection under Picture Hesitant Fuzzy Environment Using Linguistic Variables

Ambrin

Ibrar

Sen

et al. 2021

Journal of Mathematics

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The main purpose of this planned manuscript is to establish an algorithm for the solution of multiattribute decision-making (MADM) issues, where the experts utilizing linguistic variables provide the information about attributes in the form of picture hesitant fuzzy numbers. So, for the solution of these kinds of issues, we develop the TOPSIS algorithm under picture hesitant fuzzy environment using linguistic variables, which plays a vital role in practical applications, notably MADM issues, where the decision information is arranged by the decision-makers (DMs) in the form of picture hesitant fuzzy numbers. Finally, a sample example is given as an application and appropriateness of the planned method. At the end, we conduct comparison analysis of the planned method with picture fuzzy TOPSIS method and intuitionistic fuzzy TOPSIS method.

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Multiple attribute decision making problem using GRA method with incomplete weight information based on picture hesitant fuzzy setting

Cited by 36 publications

References 37 publications

A novel approach to MADM problems using Fermatean fuzzy Hamacher prioritized aggregation operators

A novel approach to MADM problems using Fermatean fuzzy Hamacher prioritized aggregation operators

Portfolio selection as a multicriteria group decision making in Pythagorean fuzzy environment with GRA and FAHP framework

Extended TOPSIS Method for Supplier Selection under Picture Hesitant Fuzzy Environment Using Linguistic Variables

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