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An Improved ARAS Approach with T-Spherical Fuzzy Information and Its Application in Multi-attribute Group Decision-Making

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Abstract

The additive ratio assessment system (ARAS) method is an effective technique for simplifying complex decision problems by determining the optimal alternative through the relative index (utility degree) to the ideal solution. However, there are still some shortcomings in the existing researches on the extension of this method when it is utilized in different decision environments, such as ignoring the correlation relationship between attributes, the lack of flexibility in the utilization of the decision process, and the relative index to the ideal solution may be scaled up or down with the ratio form. In order to overcome these disadvantages, this paper proposes the novel T-spherical fuzzy (TSF) cross entropy (TSFCE) measure and T-spherical Aczel-Alsina Heronian mean (TSFAAHM) aggregation operators and uses them to improve the ARAS method in the TSF environment. For the TSF multiple attribute group decision-making (MAGDM) problems, a group decision making model based on the improved ARAS is designed. In this model, the experts’ weights are obtained by the TSFCE-based similarity measure. The attribute combined weights are calculated by fusing the objective weights obtained by TSFCE-based entropy measure and the subjective weights got by the extended stepwise weight assessment ratio analysis (SWARA) integrated with TSFCE. In the improved ARAS method, the T-spherical Aczel-Alsina Weighted Heronian mean (TSFAAWHM) operator can capture the correlation relationship between the attributes. Compared with the relative index, the TSFCE can reflect the difference between the alternatives and the ideal solution to obtain a more stable solution ranking. Lastly, an illustrative example about the sustainable supplier selection of power battery echelon utilization (PBEU) for a 5G base station is used to demonstrate the proposed method. The effectiveness, practicability and superiority of proposed method are illustrated by parameters influence and methods comparison analysis.

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Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

AA:

Aczel-Alsina

AD:

Abstinence degree

AOs:

Aggregation operators

ANP:

Analytic Network Process

ARAS:

Additive ratio assessment system

CoCoSo:

Combined Compromise Solution

COPRAS:

Complex Proportional ASsessment

DMs:

Decision-makers

EDAS:

Evaluation based on the Distance from Average Solution

ELECTRE:

Elimination Et Choice Translating Reality

FDOSM:

Fuzzy decision by opinion score method

FFS:

Fermatean fuzzy set

FS:

Fuzzy set

FWZIC:

Fuzzy-weighted zero-inconsistency

GDSMs:

Generalized dice similarity measures

GS:

Grey set

HFLS:

Hesitant fuzzy linguistic set

HFS:

Hesitant fuzzy set

HM:

Heronian mean

IFS:

Intuitionistic fuzzy set

IRS:

Interval rough set

IT2HFS:

Interval type-2 hesitant fuzzy set

IT2FS:

Interval type-2 fuzzy set

IVIFS:

Interval-valued intuitionistic fuzzy set

MAGDM:

Multiple attribute group decision-making

MARCOS:

Measurement of Alternatives and Ranking according to COmpromise Solution

MD:

Membership degree

MM:

Muirhead mean

MSM:

Maclaurin symmetric mean

MULTIMOORA:

Multi-attribute multi-objective optimization with the ratio analysis

ND:

Non-membership degree

ORESTE:

Organization, rangement et Synthèse de donnéesrelarionnelles in French

PBEU:

Power battery echelon utilization

PFS:

Picture fuzzy set

PHFS:

Probabilistic hesitant fuzzy set

PMVNNWBD:

Probability multi-valued neutrosophic normalized weighted Bonferroni distance

PMVNS:

Probability multi-valued neutrosophic set

PyFS:

Pythagorean fuzzy set

q-ROFS:

q-Rung orthopair fuzzy set

RS:

Rough set

SFS:

Spherical fuzzy set

SVNS:

Single-valued neutrosophic set

SWARA:

Stepwise weight assessment ratio analysis

TN, TCN:

t-Norm, t-conorm

TODIM:

Interactive and multi-attribute decision making in Portuguese

TOPSIS:

Technique for order preference by similarity to ideal solution

TSF:

T-spherical fuzzy

TSFAAHM:

T-spherical Aczel-Alsina Heronian mean

TSFAAWHM:

T-spherical Aczel-Alsina Weighted Heronian mean

TSFCE:

T-spherical fuzzy cross entropy

TSFNs:

T-spherical fuzzy numbers

TSFS:

T-spherical fuzzy set

TSFSJS:

T-spherical fuzzy set Jensen-Shannon

TSFWAI:

T-spherical fuzzy weighted averaging interaction

TSFWGI:

T-spherical fuzzy weighted geometric interaction

UDHLTS:

Unbalanced double hierarchy linguistic term set

VIKOR:

VIšekriterijumsko KOmpromisno Rangiranje in Serbian

WASPAS:

Weighted Aggregated Sum Product ASessment

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Acknowledgements

This work is supported by the Humanities and Social Sciences Foundation of Ministry of Education of the People’s Republic of China (Grant No.19YJC630164), the Jiangxi Province University Humanities and Social Sciences Foundation (Grant No. GL23104), the National Natural Science Foundation, China (Grant No.71862025,72361026) and Jiangxi Provincial “Double Thousand Plan” Philosophy and Social Science Leading Talent Project (jxsq2019203008). The authors are also thankful to the Office of Research, Innovation, and Commercialization (ORIC) of Riphah International University Lahore for supporting this research under the project R-ORIC-23/FEAS/CIP-793.

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Correspondence to Liangqing Feng.

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Appendix

Appendix

The following symmetric form of T-spherical fuzzy cross-entropy measures SCEi(δ1,δ2) and seven TSF CE measures on TSFNs are all based on the Yang and Pang [17] definitions. Symmetric form of T-spherical fuzzy cross-entropy measures SCEi(δ1,δ2) (i = 1,2,…,8) can be defined as

$$SCE_{i} (\delta_{1} ,\delta_{2} ) = CE_{i} (\delta_{1} ,\delta_{2} ) + CE_{i} (\delta_{2} ,\delta_{1} )$$
$$CE_{1} (\delta_{1} ,\delta_{2} ) = \tau_{1} \log_{2} \frac{{2\tau_{1} }}{{\tau_{1} + \tau_{2} }} + \eta_{1} \log_{2} \frac{{2\eta_{1} }}{{\eta_{1} + \eta_{2} }} + \vartheta_{1} \log_{2} \frac{{2\vartheta_{1} }}{{\vartheta_{1} + \vartheta_{2} }}$$
$$\begin{gathered} CE_{2} (\delta_{1} ,\delta_{2} ) = \tau_{1} \log_{2} \frac{{2\tau_{1} }}{{\tau_{1} + \tau_{2} }} + \left( {1 - \tau_{1} } \right)\log_{2} \frac{{1 - \tau_{1} }}{{1 - 0.5(\tau_{1} + \tau_{2} )}} \hfill \\ \, + \eta_{1} \log_{2} \frac{{2\eta_{1} }}{{\eta_{1} + \eta_{2} }} + \left( {1 - \eta_{1} } \right)\log_{2} \frac{{1 - \eta_{1} }}{{1 - 0.5(\eta_{1} + \eta_{2} )}} \hfill \\ \, + \vartheta_{1} \log_{2} \frac{{2\vartheta_{1} }}{{\vartheta_{1} + \vartheta_{2} }} + \left( {1 - \vartheta_{1} } \right)\log_{2} \frac{{1 - \vartheta_{1} }}{{1 - 0.5(\vartheta_{1} + \vartheta_{2} )}} \hfill \\ \end{gathered}$$
$$CE_{3} (\delta_{1} ,\delta_{2} ) = \left( {\left( {\tau_{1} \log_{2} \frac{{2\tau_{1} }}{{\tau_{1} + \tau_{2} }}} \right)^{\lambda } + \left( {\eta_{1} \log_{2} \frac{{2\eta_{1} }}{{\eta_{1} + \eta_{2} }}} \right)^{\lambda } + \left( {\vartheta_{1} \log_{2} \frac{{2\vartheta_{1} }}{{\vartheta_{1} + \vartheta_{2} }}} \right)^{\lambda } } \right)^{{{1 \mathord{\left/ {\vphantom {1 \lambda }} \right. \kern-0pt} \lambda }}}$$
$$CE_{4} (\delta_{1} ,\delta_{2} ) = \left( \begin{gathered} \left( {\tau_{1} \log_{2} \frac{{2\tau_{1} }}{{\tau_{1} + \tau_{2} }}} \right)^{\lambda } + \left( {\left( {1 - \tau_{1} } \right)\log_{2} \frac{{1 - \tau_{1} }}{{1 - 0.5(\tau_{1} + \tau_{2} )}}} \right)^{\lambda } \hfill \\ + \left( {\eta_{1} \log_{2} \frac{{2\eta_{1} }}{{\eta_{1} + \eta_{2} }}} \right)^{\lambda } + \left( {\left( {1 - \eta_{1} } \right)\log_{2} \frac{{1 - \eta_{1} }}{{1 - 0.5(\eta_{1} + \eta_{2} )}}} \right)^{\lambda } \hfill \\ + \left( {\vartheta_{1} \log_{2} \frac{{2\vartheta_{1} }}{{\vartheta_{1} + \vartheta_{2} }}} \right)^{\lambda } + \left( {\left( {1 - \vartheta_{1} } \right)\log_{2} \frac{{1 - \vartheta_{1} }}{{1 - 0.5(\vartheta_{1} + \vartheta_{2} )}}} \right)^{\lambda } \hfill \\ \end{gathered} \right)^{{{1 \mathord{\left/ {\vphantom {1 \lambda }} \right. \kern-0pt} \lambda }}}$$
$$\begin{gathered} CE_{5} (\delta_{1} ,\delta_{2} ) = \frac{{1 + \tau_{1} - \eta_{1} - \vartheta_{1} }}{2}\log_{2} \left( {\frac{{2(1 + \tau_{1} - \eta_{1} - \vartheta_{1} )}}{{2 + \tau_{1} - \eta_{1} - \vartheta_{1} + \tau_{2} - \eta_{2} - \vartheta_{2} }}} \right) \hfill \\ \, + \frac{{1 - \tau_{1} + \eta_{1} + \vartheta_{1} }}{2}\log_{2} \left( {\frac{{2(1 - \tau_{1} + \eta_{1} + \vartheta_{1} )}}{{2 - \tau_{1} + \eta_{1} + \vartheta_{1} - \tau_{2} + \eta_{2} + \vartheta_{2} }}} \right) \hfill \\ \end{gathered}$$
$$CE_{6} (\delta_{1} ,\delta_{2} ) = \frac{{\tau_{1}^{q} + \tau_{2}^{q} }}{2} - \left( {\frac{{\tau_{1} + \tau_{2} }}{2}} \right)^{q} + \frac{{\eta_{1}^{q} + \eta_{2}^{q} }}{2} - \left( {\frac{{\eta_{1} + \eta_{2} }}{2}} \right)^{q} + \frac{{\vartheta_{1}^{q} + \vartheta_{2}^{q} }}{2} - \left( {\frac{{\vartheta_{1} + \vartheta_{2} }}{2}} \right)^{q}$$
$$\begin{gathered} CE_{7} (\delta_{1} ,\delta_{2} ) = \left( {\frac{1}{{1 - 2^{1 - q} }}\left( {\frac{{\tau_{1}^{q} + \tau_{2}^{q} }}{2} - \left( {\frac{{\tau_{1} + \tau_{2} }}{2}} \right)^{q} } \right)^{\lambda } } \right)^{{{1 \mathord{\left/ {\vphantom {1 \lambda }} \right. \kern-0pt} \lambda }}} + \left( {\frac{1}{{1 - 2^{1 - q} }}\left( {\frac{{\eta_{1}^{q} + \eta_{2}^{q} }}{2} - \left( {\frac{{\eta_{1} + \eta_{2} }}{2}} \right)^{q} } \right)^{\lambda } } \right)^{{{1 \mathord{\left/ {\vphantom {1 \lambda }} \right. \kern-0pt} \lambda }}} \hfill \\ \, + \left( {\frac{1}{{1 - 2^{1 - q} }}\left( {\frac{{\vartheta_{1}^{q} + \vartheta_{2}^{q} }}{2} - \left( {\frac{{\vartheta_{1} + \vartheta_{2} }}{2}} \right)^{q} } \right)^{\lambda } } \right)^{{{1 \mathord{\left/ {\vphantom {1 \lambda }} \right. \kern-0pt} \lambda }}} \hfill \\ \end{gathered}$$

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Wang, H., Xu, T., Feng, L. et al. An Improved ARAS Approach with T-Spherical Fuzzy Information and Its Application in Multi-attribute Group Decision-Making. Int. J. Fuzzy Syst. (2024). https://doi.org/10.1007/s40815-024-01718-y

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