Intuitionistic Linguistic Multiple Attribute Decision-making Based on Heronian Mean Method and Its Application to Evaluation of Scientific Research Capacity

Zhang, Chonghui; Su, Weihua; Zeng, Shouzhen

doi:10.12973/ejmste/77933

Cited by 5 publications

(17 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Table , it can be seen clearly that the ranking result by the proposed method is absolutely identical with those presented by other methods in Refs. and , i.e.,

A_{2} ≻ A_{4} ≻ A_{1} ≻ A_{3}

. In accordance with comparative analysis, we verify the rationality of the proposed method.…”

Section: Practical Applicationsupporting

confidence: 67%

“…In addition, from Table , we find that the ranking result of our proposed method (

k = 2

) is same as the ranking results of Ju et al.’s method (

k = 2

) and Zhang et al.’s method (

p = 1, q = 1

) in Example 5.2, i.e.,

A_{2} ≻ A_{4} ≻ A_{1} ≻ A_{3}

. These two methods are the typical representative of methods considering interrelationship of attributes.…”

Section: Practical Applicationsupporting

confidence: 59%

“…The purpose of this section is to illustrate the rationality and availability of the proposed method based on the PFLPWMSM operator by the comparison with other existing MAGDM methods, such as Peng and Yang's method based on the Pythagorean fuzzy linguistic weighted averaging (PFLWA) operator, Liu and Wang's method based on intuitionistic linguistic power generalized ordered weighted average (ILPGOWA) operator, Ju et al.’s method based on weighted intuitionistic linguistic Maclaurin symmetric mean (WILMSM) operator, and Zhang et al.’s method based on intuitionistic linguistic weighted Heronian mean (ILWHM) operator . To demonstrate its effectiveness more visually, some examples are used to show the application scope of it expands.…”

Section: Practical Applicationmentioning

confidence: 99%

“…To further highlight the advantage of our proposed method, i.e., eliminating the influence of extreme values from the bias DMs, we applied our proposed method, Ju et al.’s method, Zhang et al.’s method, and Liu and Wang's method to solve the new practical application and compare the ranking results by these methods.…”

Section: Practical Applicationmentioning

confidence: 99%

“…Based on Table , we find that the extreme values

{\overset{\tilde{r}}{true}}^{1}_{22} = false⟨ s_{1}, (0.4, 0.5) false⟩

and

{\overset{\tilde{r}}{true}}^{1}_{42} = false⟨ s_{6}, (0.2, 0.8) false⟩

have an effect on ranking results of four alternative projects by these methods as mentioned in Refs. and . The ranking results by Ju et al.’s method and Zhang et al.’s method are changed from

A_{2} ≻ A_{4} ≻ A_{1} ≻ A_{3}

A_{4} ≻ A_{1} ≻ A_{2} ≻ A_{3}

, so the best projects also are changed from A 2 to A 4 .…”

Section: Practical Applicationmentioning

confidence: 99%

See 4 more Smart Citations

Some power Maclaurin symmetric mean aggregation operators based on Pythagorean fuzzy linguistic numbers and their application to group decision making

Teng

Liu

Лю

2018

Int. J. Intell. Syst.

View full text Add to dashboard Cite

The power average (PA) operator and Maclaurin symmetric mean (MSM) operator are two important tools to handle the multiple attribute group decision‐making (MAGDM) problems, and the combination of two operators can eliminate the influence of unreasonable information from biased decision makers (DMs) and can capture the interrelationship among any number of arguments. The Pythagorean fuzzy linguistic set (PFLS) is parallel to the intuitionistic linguistic set (ILS), which is more powerful to convey the uncertainty and ambiguity of the DMs than ILS. In this paper, we propose some power MSM aggregation operators for Pythagorean fuzzy linguistic information, such as Pythagorean fuzzy linguistic power MSM operator and Pythagorean fuzzy linguistic power weighted MSM (PFLPWMSM) operator. At the same time, we further discuss the properties and special cases of these operators. Then, we propose a new method to solve the MAGDM problems with Pythagorean fuzzy linguistic information based on the PFLPWMSM operator. Finally, some illustrative examples are utilized to show the effectiveness of the proposed method.

show abstract

“…As shown in Table , it can be seen clearly that the ranking result by the proposed method is absolutely identical with those presented by other methods in Refs. and , i.e.,

A_{2} ≻ A_{4} ≻ A_{1} ≻ A_{3}

. In accordance with comparative analysis, we verify the rationality of the proposed method.…”

Section: Practical Applicationsupporting

confidence: 67%

“…In addition, from Table , we find that the ranking result of our proposed method (

k = 2

) is same as the ranking results of Ju et al.’s method (

k = 2

) and Zhang et al.’s method (

p = 1, q = 1

) in Example 5.2, i.e.,

A_{2} ≻ A_{4} ≻ A_{1} ≻ A_{3}

. These two methods are the typical representative of methods considering interrelationship of attributes.…”

Section: Practical Applicationsupporting

confidence: 59%

Section: Practical Applicationmentioning

confidence: 99%

Section: Practical Applicationmentioning

confidence: 99%

“…Based on Table , we find that the extreme values

{\overset{\tilde{r}}{true}}^{1}_{22} = false⟨ s_{1}, (0.4, 0.5) false⟩

and

{\overset{\tilde{r}}{true}}^{1}_{42} = false⟨ s_{6}, (0.2, 0.8) false⟩

have an effect on ranking results of four alternative projects by these methods as mentioned in Refs. and . The ranking results by Ju et al.’s method and Zhang et al.’s method are changed from

A_{2} ≻ A_{4} ≻ A_{1} ≻ A_{3}

A_{4} ≻ A_{1} ≻ A_{2} ≻ A_{3}

, so the best projects also are changed from A 2 to A 4 .…”

Section: Practical Applicationmentioning

confidence: 99%

See 3 more Smart Citations

Some power Maclaurin symmetric mean aggregation operators based on Pythagorean fuzzy linguistic numbers and their application to group decision making

Teng

Liu

Лю

2018

Int. J. Intell. Syst.

View full text Add to dashboard Cite

show abstract

A Nonradial Super Efficiency DEA Framework Using a MCDM to Measure the Research Efficiency of Disciplines at Chinese Universities

Ding-xin

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Evaluating the efficiency of scientific research plays an important role in accelerating technological innovation and optimizing the allocation of research resources. Most studies have focused on measuring research efficiency from a macro perspective, ignoring differences within disciplines. Furthermore, existing methods have failed to discriminate between evaluation results and the fact that research has variable returns to scale. To address this, in this paper we propose a multiple-criteria decision-making (MCDM) nonradial super efficiency data envelopment analysis (NRSDEA) model, which uses an outputoriented nonradial SDEA method to manage nonsolution problems and integer decision variable constraints. In addition, we used a Malmquist index to decompose the productivity changes of statistics discipline's research efficiency at different universities in China. Finally, we verified the rationality and effectiveness of the proposed method. INDEX TERMS Discipline research efficiency, multiple-criteria decision-making, nonradial SDEA, Malmquist index.

show abstract

Financing Selection Method in Discipline Evaluation Using a Weighted Induced Model

Chen¹,

Jin²,

Chen³

et al. 2019

E+M

Self Cite

View full text Add to dashboard Cite

Intuitionistic Linguistic Multiple Attribute Decision-making Based on Heronian Mean Method and Its Application to Evaluation of Scientific Research Capacity

Cited by 5 publications

References 26 publications

Some power Maclaurin symmetric mean aggregation operators based on Pythagorean fuzzy linguistic numbers and their application to group decision making

Some power Maclaurin symmetric mean aggregation operators based on Pythagorean fuzzy linguistic numbers and their application to group decision making

A Nonradial Super Efficiency DEA Framework Using a MCDM to Measure the Research Efficiency of Disciplines at Chinese Universities

Financing Selection Method in Discipline Evaluation Using a Weighted Induced Model

Contact Info

Product

Resources

About