A novel interval-valued neutrosophic EDAS method: prioritization of the United Nations national sustainable development goals
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Evaluation based on distance from average solution (EDAS) method is based on the distances of each alternative from the average solution with respect to each criterion. This method is similar to distance-based multi-criteria decision-making methods such as TOPSIS and VIKOR. It simplifies the calculation of distances to the deal solution and determines the final decision rapidly. EDAS method has been already extended to its ordinary fuzzy, intuitionistic fuzzy and type-2 fuzzy versions. In this paper, we extend EDAS method to its interval-valued neutrosophic version with the advantage of considering a expert’s truthiness, falsity, and indeterminacy simultaneously. The proposed method has been applied to the prioritization of United Nations national sustainable development goals, and one-at-a-time sensitivity analysis is conducted to check the robustness of the given decisions. The proposed method is also compared with the intuitionistic fuzzy TOPSIS method for its validity.
KeywordsEDAS Interval-valued neutrosophic sets Multi-criteria United Nations Deneutrosophication
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Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
- Bausys R, Zavadskas EK (2015) Multicriteria decision making approach by VIKOR under interval neutrosophic set environment. Econ Comput Econ Cybern Stud Res 49(4):33–48Google Scholar
- Bausys R, Zavadskas EK, Kaklauskas A (2015) Application of neutrosophic set to multicriteria decision making by COPRAS. Econ Comput Econ Cybern Stud Res 49(2):91–106Google Scholar
- Deli I, Şubaş Y, Smarandache F, Ali M (2016) Interval valued bipolar fuzzy weighted neutrosophic sets and their application. In: 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE)Google Scholar
- Elhassouny A, Smarandache F (2016) Neutrosophic-simplified-TOPSIS multi-criteria decision-making using combined simplified-TOPSIS method and neutrosophics. In: 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE)Google Scholar
- Jahan S (2017) Human Development Report 2016-Human Development for Everyone (No. id: 12021)Google Scholar
- Ji P, Zhang HY, Wang JQ (2016) A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection. Neural Comput Appl 1–14Google Scholar
- Karaşan A, Kahraman C (2017) Interval-valued neutrosophic extension of EDAS method. In: Advances in fuzzy logic and technology. Springer, Berlin, pp 343–357Google Scholar
- OECD (2007) 2007 Annual report on sustainable development work in the OECD, ParisGoogle Scholar
- Opricovic S (1998) Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, BelgradeGoogle Scholar
- Otay İ, Kahraman C (2017) Six sigma project selection using interval neutrosophic TOPSIS. In: Advances in fuzzy logic and technology. Springer, Berlin, pp 83–93Google Scholar
- Peng H, Zhang H, Wang J (2016) Probability multi-valued neutrosophic sets and its application in multi-criteria group decision-making problems. Neural Comput & Applic. https://doi.org/10.1007/s00521-016-2702-0
- Rubio E, Castillo O, Valdez F, Melin P, Gonzalez CI, Martinez G (2017) An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques. Adv Fuzzy Syst 2017:7094046. https://doi.org/10.1155/2017/7094046.
- Sambuc R (1975) Fonctions \(\Phi \)-floues. Application l’aide au diagnostic en pathologie thyroidienne. Univ. Marseille, MarseilleGoogle Scholar
- Smarandache F (2006) Neutrosophic set – a generalization of the intuitionistic fuzzy set. IEEE Int Conf Granular Comput 38–42. https://doi.org/10.1109/GRC.2006.1635754
- Zhang HY, Wang JQ, Chen XH (2014) Interval neutrosophic sets and their application in multicriteria decision-making problems. Sci World J 2014:15Google Scholar
- Zimmermann HJ (2011) Fuzzy set theory—and its applications. Springer, New YorkGoogle Scholar