A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined Weights Article Swipe
Haolun Wang
,
Wei Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.59543/comdem.v1i.10263
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.59543/comdem.v1i.10263
Compared with the existing fuzzy numbers, normal T-spherical fuzzy numbers (NTSFNs) inherit the merits of T-spherical fuzzy numbers and normal fuzzy numbers, and can describe normal distribution phenomena and neutral information at the same time. It not only has a large expression domain, but also has a strong ability to handle indeterminacy and vagueness. In this article, the main purpose is to introduce a novel distance measure and improve the ARAS (Additive Ratio ASsessment) method in the NTSF context to solve the group decision-making problem with combined weight information and make up for the shortcomings of the existing ARAS approaches, for example, correlations between attributes are ignored, the decision process is inflexible, and ARAS has not been extended in the NTSF environment, etc. First, we define a Hamming distance measure with NTSFNs, and propose several Aczel-Alsina operational laws of NTSFNs. Then, we develop the normal T-spherical fuzzy Aczel-Alsina Heronian mean (NTSFAAHM) operator and its weighted form (NTSFAAWHM), and related properties and special cases are discussed. Third, For the NTSF multi-attribute group decision-making (MAGDM) problems, we define the NTSF similarity, improve SWARA(Stepwise Weight Assessment Ratio Analysis) and build MDM (maximizing deviation model) to calculate expert weight and attribute subjective and objective weight respectively on the basis of NTSF Hamming distance. Furthermore, we integrate the NTSFAAWHM operator and Hamming distance with ARAS method to form a novel alternative ranking technique, namely NTSF ARAS-H method. Lastly, a numerical example of investment decision for internet waste clothing recycling platform (IWCRP) is presented to show the feasibility of our methodology, the reliability, effectiveness and rationality are verified via the sensitivity and comparative analysis.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.59543/comdem.v1i.10263
- https://comdem.org/index.php/comdem/article/download/10263/7905
- OA Status
- hybrid
- Cited By
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403994712
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403994712Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.59543/comdem.v1i.10263Digital Object Identifier
- Title
-
A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined WeightsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-02Full publication date if available
- Authors
-
Haolun Wang, Wei ZhaoList of authors in order
- Landing page
-
https://doi.org/10.59543/comdem.v1i.10263Publisher landing page
- PDF URL
-
https://comdem.org/index.php/comdem/article/download/10263/7905Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://comdem.org/index.php/comdem/article/download/10263/7905Direct OA link when available
- Concepts
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Group decision-making, Group (periodic table), Fuzzy logic, Mathematics, Computer science, Combinatorics, Mathematical optimization, Pure mathematics, Algebra over a field, Artificial intelligence, Physics, Psychology, Social psychology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 8Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.deviation | 189 |
| abstract_inverted_index.distance. | 208 |
| abstract_inverted_index.integrate | 211 |
| abstract_inverted_index.introduce | 62 |
| abstract_inverted_index.numerical | 234 |
| abstract_inverted_index.objective | 199 |
| abstract_inverted_index.phenomena | 27 |
| abstract_inverted_index.presented | 247 |
| abstract_inverted_index.problems, | 173 |
| abstract_inverted_index.recycling | 243 |
| abstract_inverted_index.(NTSFAAHM) | 150 |
| abstract_inverted_index.Assessment | 182 |
| abstract_inverted_index.attributes | 104 |
| abstract_inverted_index.discussed. | 164 |
| abstract_inverted_index.expression | 41 |
| abstract_inverted_index.investment | 237 |
| abstract_inverted_index.properties | 159 |
| abstract_inverted_index.subjective | 197 |
| abstract_inverted_index.technique, | 227 |
| abstract_inverted_index.vagueness. | 53 |
| abstract_inverted_index.(maximizing | 188 |
| abstract_inverted_index.ASsessment) | 73 |
| abstract_inverted_index.T-spherical | 7, 15, 145 |
| abstract_inverted_index.alternative | 225 |
| abstract_inverted_index.approaches, | 99 |
| abstract_inverted_index.comparative | 266 |
| abstract_inverted_index.feasibility | 251 |
| abstract_inverted_index.inflexible, | 111 |
| abstract_inverted_index.information | 30, 88 |
| abstract_inverted_index.operational | 136 |
| abstract_inverted_index.rationality | 259 |
| abstract_inverted_index.sensitivity | 264 |
| abstract_inverted_index.similarity, | 178 |
| abstract_inverted_index.(NTSFAAWHM), | 156 |
| abstract_inverted_index.Aczel-Alsina | 135, 147 |
| abstract_inverted_index.Furthermore, | 209 |
| abstract_inverted_index.correlations | 102 |
| abstract_inverted_index.distribution | 26 |
| abstract_inverted_index.environment, | 121 |
| abstract_inverted_index.methodology, | 254 |
| abstract_inverted_index.reliability, | 256 |
| abstract_inverted_index.respectively | 201 |
| abstract_inverted_index.shortcomings | 94 |
| abstract_inverted_index.effectiveness | 257 |
| abstract_inverted_index.indeterminacy | 51 |
| abstract_inverted_index.SWARA(Stepwise | 180 |
| abstract_inverted_index.decision-making | 83, 171 |
| abstract_inverted_index.multi-attribute | 169 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.96844873 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |