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A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets
Abstract
The paper introduces the concept of simplified neutrosophic sets (SNSs), which are a subclass of neutrosophic sets, and defines the operational laws of SNSs. Then, we propose some aggregation operators, including a simplified neutrosophic weighted arithmetic average operator and a simplified neutrosophic weighted geometric average operator. Based on the two aggregation operators and cosine similarity measure for SNSs, a multicriteria decision-making method is established in which the evaluation values of alternatives with respective to criteria are represented by the form of SNSs. The ranking order of alternatives is performed through the cosine similarity measure between an alternative and the ideal alternative and the best one(s) can be determined as well. Finally, a numerical example shows the application of the proposed method.
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Citations
Cites
Atanassov, K. T. (1986) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(86)80034-3 · Cited by 16,465
Cited by
TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment
Biswas, P., Pramanik, S., & Giri, B. C. (2016) · Neural Computing and Applications · doi:10.1007/s00521-015-1891-2 · Cited by 408
Liu, P., & Wang, Y. (2014) · Neural Computing and Applications · doi:10.1007/s00521-014-1688-8 · Cited by 306
Metadata
- Authors
- Ye, J.
- Venue
- Journal of Intelligent & Fuzzy Systems
- Publisher
- SAGE Publications
- Published
- 2014-01
- Volume
- 26
- Issue
- 5
- Pages
- 2459-2466
- ISSN
- 1064-1246 (ISSN-L)
- 1875-8967
- OpenAlex
- W1595461165
- Cited by
- 646
- References
- 1
- FWCI
- 65.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ye, J. (2014). A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. Journal of Intelligent & Fuzzy Systems. https://doi.org/10.3233/ifs-130916
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
