Multi-attribute Group Decision-making for Online Education Live Platform Selection Based on Linguistic Intuitionistic Cubic Fuzzy Aggregation Operators

Liu, Haobin; Liu, Yi; Xu, Lei; Abdullah, Saleem

doi:10.21203/rs.3.rs-29617/v1

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…What's more, when the decision alternative has more attributes, we can use PCA 38 method and IG 39 method to reduce the attributes dimension, and then make the decision based on this operator. Moreover, the application in several other fields will be considered, including engineering, 40 business, and education 41 …”

Section: Discussionmentioning

confidence: 99%

Interval numbers BON ^r,q ‐OWA operator and its application to multiattribute decision‐making

Zeng

et al. 2021

Int J of Intelligent Sys

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Interval numbers multiple attribute decision‐making (MADM) is an important branch of uncertainty decision theory, and the decision result largely depends on the selection of the aggregation operator. In this paper, we analyze the ordered weighted average (OWA) operator, which is an averaging aggregation operator. The OWA operator provides an aggregation method between the minimum and maximum operators. Moreover, we further analyze some of extensions about OWA operator, and pay special attention to the Bonferroni means and OWA (BON‐OWA) operator. Note that the BON‐OWA operator only aggregate the input arguments which are exact numbers. Under normal circumstances, decision makers is difficult to provide a clear evaluation value for attribute and most of them are described by vague information. When the decision information is an interval numbers, the BON‐OWA operator cannot describe decision result accurately. Under these environments, we proposed the interval numbers BON‐OWA (IBr,q‐OWA) operator to deal with the vague decision information in this paper. Then we consider their main properties, such as idempotence, monotonicity, and boundedness and prove them. Besides, a wide range of special aggregation operators are found in changing parameter values, such as the square mean and max operator, and so on. We also compare the ranking method of the interval numbers based on Boolean matrix. As a result, the combination of IBr,q‐OWA operator makes the decision result more scientific. Finally, a new approach for decision‐making problem is developed based on the IBr,q‐OWA operator, which shows the effectiveness in practical examples.

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Section: Discussionmentioning

confidence: 99%

Interval numbers BON ^r,q ‐OWA operator and its application to multiattribute decision‐making

Zeng

et al. 2021

Int J of Intelligent Sys

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“…Ye [43][44][45] introduced [46] weighted aggregation operators for MCDM with interval-valued hesitant fuzzy sets, single-valued neutrosophic sets, and simplified neutrosophic sets. Liu et al [47] introduced linguistic intuitionistic cubic fuzzy AOs, Abdullah et al [48] presented a novel concept of sine trigonometric picture fuzzy AOs, and Naeem et al [49] initiated the concept of similarity measures for fractional orthotriple fuzzy sets and their applications towards MCDM. Ashraf et al [50] initiated the novel concept of spherical fuzzy sets.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems

Iampan

Santos‐García

Riaz

et al. 2021

Journal of Mathematics

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The linear Diophantine fuzzy set (LDFS) has been proved to be an efficient tool in expressing decision maker (DM) evaluation values in multicriteria decision-making (MCDM) procedure. To more effectively represent DMs’ evaluation information in complicated MCDM process, this paper proposes a MCDM method based on proposed novel aggregation operators (AOs) under linear Diophantine fuzzy set (LDFS). A q -Rung orthopair fuzzy set ( q -ROFS), Pythagorean fuzzy set (PFS), and intuitionistic fuzzy set (IFS) are rudimentary concepts in computational intelligence, which have diverse applications in modeling uncertainty and MCDM. Unfortunately, these theories have their own limitations related to the membership and nonmembership grades. The linear Diophantine fuzzy set (LDFS) is a new approach towards uncertainty which has the ability to relax the strict constraints of IFS, PFS, and q –ROFS by considering reference/control parameters. LDFS provides an appropriate way to the decision experts (DEs) in order to deal with vague and uncertain information in a comprehensive way. Under these environments, we introduce several AOs named as linear Diophantine fuzzy Einstein weighted averaging (LDFEWA) operator, linear Diophantine fuzzy Einstein ordered weighted averaging (LDFEOWA) operator, linear Diophantine fuzzy Einstein weighted geometric (LDFEWG) operator, and linear Diophantine fuzzy Einstein ordered weighted geometric (LDFEOWG) operator. We investigate certain characteristics and operational laws with some illustrations. Ultimately, an innovative approach for MCDM under the linear Diophantine fuzzy information is examined by implementing suggested aggregation operators. A useful example related to a country’s national health administration (NHA) to create a fully developed postacute care (PAC) model network for the health recovery of patients suffering from cerebrovascular diseases (CVDs) is exhibited to specify the practicability and efficacy of the intended approach.

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Value, Dilemmas and Innovative Strategies of Educational Management Clustering in the Internet Era

Tan

2024

Applied Mathematics and Nonlinear Sciences

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With the continuous improvement of the national education system, the Internet brings new opportunities for educational development but also brings specific challenges to the educational management of colleges and universities. This paper aims to analyze the current situation of education management in colleges and universities and construct an innovation model for education management clustering based on PDCA, which will be explored for its development value and power. And then, using the partial least squares regression algorithm and its modeling steps, to construct the educational management clustering regression model. The influencing factors of the educational management clustering model are analyzed with 12 “double first-class” colleges and universities in five provinces in Northwest China as research objects. The results show that the average comprehensive efficiency and pure technical efficiency of the universities in Northwest China range from 0.6585 to 0.9987 and from 0.773 to 0.994, respectively, indicating that the efficiency of higher education resource allocation in Northwest China is constantly improving. Compared with the traditional education management model, the colleges and universities applying the education management clustering model constructed in this paper in 2022–2023 show a scale-invariant trend, indicating that the colleges and universities have sufficiently invested in education resources and realized the scale efficiency. Educational management clustering can improve the efficiency of school educational resource allocation, thus improving the level of educational management and providing a theoretical basis for the study of innovative strategies in educational management.

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Multi-attribute Group Decision-making for Online Education Live Platform Selection Based on Linguistic Intuitionistic Cubic Fuzzy Aggregation Operators

Cited by 4 publications

References 41 publications

Interval numbers BON ^r,q ‐OWA operator and its application to multiattribute decision‐making

Interval numbers BON ^r,q ‐OWA operator and its application to multiattribute decision‐making

Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems

Value, Dilemmas and Innovative Strategies of Educational Management Clustering in the Internet Era

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Multi-attribute Group Decision-making for Online Education Live Platform Selection Based on Linguistic Intuitionistic Cubic Fuzzy Aggregation Operators

Cited by 4 publications

References 41 publications

Interval numbers BON r,q ‐OWA operator and its application to multiattribute decision‐making

Interval numbers BON r,q ‐OWA operator and its application to multiattribute decision‐making

Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems

Value, Dilemmas and Innovative Strategies of Educational Management Clustering in the Internet Era

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Product

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Interval numbers BON ^r,q ‐OWA operator and its application to multiattribute decision‐making

Interval numbers BON ^r,q ‐OWA operator and its application to multiattribute decision‐making