A Double Interactive Alternative Reduction Approach for Probabilistic Linguistic Multi-criteria Decision-Making with Incomplete Criteria Weight Information

Yue, Na; Xie, Jiancang; Chen, Shui-Li

doi:10.1007/978-981-15-2459-2_6

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…This methodology must address the complexities of numbers, ratings, and weights of various indicators. Recently, commonly used methods for MCGDM problems include VIKOR (vlsekriterijumska optimizacija i kompromisno resenje in serbian) [ 20 ], AHP (analytic hierarchy process) [ 21 ], ELECTRE (ELimination Et Choix Traduisant la REalit - ELimination and Choice Expressing the REality) [ 22 , 23 ], TOPSIS [ 24 ] (technique for order preference by similarity to ideal solution), and other methods [ 25 – 29 ]. These methods have some disadvantages: for VIKOR, the ranking results are sensitive on the values of the weight for strategy of maximum group utility.…”

Section: Introductionmentioning

confidence: 99%

Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

Xie

Honghui

et al. 2022

Appl Intell

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During the COVID-19, colleges organized online education on a massive scale. To make better use of online education in the post-epidemic era, this paper conducts an online education satisfaction survey with four types of colleges and 129,325 students propose a fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method based on the cloud model to rank the satisfaction of different colleges. Firstly, based on the characteristics of online education during the COVID-19, we build an evaluation indicator system from four dimensions: technology, instructor, learner and environment including, 10 indicators and 94 sub-indicators. Secondly, the cloud model is used to quantitatively describe the natural language and uncertainty in a large amount of assessment information. The cloud model generator is used for sub-indicators and achieves an effective and flexible conversion between linguistic information and quantitative values. The cloud model of indicators are presented by integrating the corresponding sub-indicators. The weights of indicators are determined by the entropy method based on the cloud model and possibility degree matrix, which eliminates the judgment of decision-makers and has great power for handling practical problems with unknown weight information. Finally, a fuzzy TOPSIS method based on the cloud model is proposed to rank the satisfaction of online education of different colleges. The proposed method is compared with other existing methods to shown its merits. The experimental result is consistent with the proportion of students who accept online education in the post-epidemic era. According to the second questionnaire, as the qualitative evaluation of the cloud model of indicators increases, the qualitative evaluation of satisfaction of different types of colleges will also increase. It indicates that the method proposed in this paper is practical.

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Section: Introductionmentioning

confidence: 99%

Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

Xie

Honghui

et al. 2022

Appl Intell

Self Cite

View full text Add to dashboard Cite

show abstract

“…Liao et al (2020a, b) constructed the incomplete probabilistic linguistic preference relation on the basis of the PLTS and the lack of some preference information. These theories give us some powerful tools to deal with uncertainty and vagueness in real applications (Lin and Xu, 2018; Xie et al , 2018; Lin et al , 2019a; Yue et al , 2019; Yue et al , 2020a, b).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic uncertain linguistic TODIM method based on the generalized Choquet integral and its application

Xie

Yue

et al. 2021

IJICC

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PurposeThe purpose of this paper is to develop a probabilistic uncertain linguistic (PUL) TODIM method based on the generalized Choquet integral, with respect to the interdependencies between criteria, for the selection of the best alternate in the context of multiple criteria group decision-making (MCGDM).Design/methodology/approachOwing to decision makers (DMs) do not always show completely rational and may have the preference of bounded rational behavior, this may affect the result of the MCGDM. At the same time, criteria interaction is a focused issue in MCGDM. Hence, a novel TODIM method based on the generalized Choquet integral selects the best alternate using PUL evaluation, where the generalized Choquet integral is used to calculate the weight of criterion. The generalized PUL distance measure between two probabilistic uncertain linguistic elements (PULEs) is calculated and the perceived dominance degree matrices for each alternate relative to other alternates are obtained. Furthermore, the comprehensive perceived dominance degree of each alternate can be calculated to get the ranking.FindingsPotential application of the PUL-TODIM method is demonstrated through an evaluation example with sensitivity and comparative analysis.Originality/valueAs per author's concern, there are no TODIM methods with probabilistic uncertain linguistic sets (PULTSs) to solve MCGDM problems under uncertainty. Compared with the result of existing methods, the final judgment value of alternates using the extended TODIM methodology is highly corroborated, which proves its potential in solving MCGDM problems under qualitative and quantitative environments.

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A Double Interactive Alternative Reduction Approach for Probabilistic Linguistic Multi-criteria Decision-Making with Incomplete Criteria Weight Information

Cited by 2 publications

References 11 publications

Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

Probabilistic uncertain linguistic TODIM method based on the generalized Choquet integral and its application

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