Advertisement

Soft Computing

, Volume 22, Issue 15, pp 4891–4906 | Cite as

A novel interval-valued neutrosophic EDAS method: prioritization of the United Nations national sustainable development goals

Focus

Abstract

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.

Keywords

EDAS Interval-valued neutrosophic sets Multi-criteria United Nations Deneutrosophication 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96CrossRefMATHGoogle Scholar
  2. 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
  3. 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
  4. Biswas P, Pramanik S, Giri BC (2016) TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Comput Appl 27(3):727–737CrossRefGoogle Scholar
  5. Castillo O, Sanchez MA, Gonzalez CI, Martinez GE (2017) Review of recent type-2 fuzzy image processing applications. Information 8(3):97CrossRefGoogle Scholar
  6. 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
  7. 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
  8. Grattan-Guiness I (1975) Fuzzy membership mapped onto interval and many-valued quantities. Z Math Logik Grundladen Math 22:149–160MathSciNetCrossRefGoogle Scholar
  9. Hu J, Pan L, Chen X (2017) An interval neutrosophic projection-based VIKOR method for selecting doctors. Cogn Comput 9(6):801–816CrossRefGoogle Scholar
  10. Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, New YorkCrossRefMATHGoogle Scholar
  11. Jahn K (1975) Intervall-wertige Mengen. Math Nach 68:115–132MathSciNetCrossRefMATHGoogle Scholar
  12. Jahan S (2017) Human Development Report 2016-Human Development for Everyone (No. id: 12021)Google Scholar
  13. 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
  14. 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
  15. Keshavarz Ghorabaee M, Zavadskas EK, Olfat L, Turskis Z (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26(3):435–451CrossRefGoogle Scholar
  16. Li Y, Wang Y, Liu P (2016) Multiple attribute group decision-making methods based on trapezoidal fuzzy two-dimension linguistic power generalized aggregation operators. Soft Comput 20(7):2689–2704CrossRefMATHGoogle Scholar
  17. Ma H, Hu Z, Li K, Zhang H (2016) Toward trustworthy cloud service selection: a time-aware approach using interval neutrosophic set. J Parallel Distrib Comput 96:75–94CrossRefGoogle Scholar
  18. OECD (2007) 2007 Annual report on sustainable development work in the OECD, ParisGoogle Scholar
  19. Opricovic S (1998) Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, BelgradeGoogle Scholar
  20. 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
  21. Park JH, Park IY, Kwun YC, Tan X (2011) Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Appl Math Model 35(5):2544–2556MathSciNetCrossRefMATHGoogle Scholar
  22. Peng JJ, Wang JQ, Zhang HY, Chen XH (2014) An outranking approach for multi-criteria decision-making problems with simplified neutrosophic sets. Appl Soft Comput 25:336–346CrossRefGoogle Scholar
  23. 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
  24. Peng J, Wang J, Wu X (2017) An extension of the ELECTRE approach with multi-valued neutrosophic information. Neural Comput & Applic 28(Suppl 1):1011–1022CrossRefGoogle Scholar
  25. Rivieccio U (2008) Neutrosophic logics: prospects and problems. Fuzzy Sets Syst 159(14):1860–1868MathSciNetCrossRefMATHGoogle Scholar
  26. 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.
  27. Sambuc R (1975) Fonctions \(\Phi \)-floues. Application l’aide au diagnostic en pathologie thyroidienne. Univ. Marseille, MarseilleGoogle Scholar
  28. Sanchez MA, Castillo O, Castro JR (2015) Information granule formation via the concept of uncertainty-based information with interval type-2 fuzzy sets representation and Takagi–Sugeno–Kang consequents optimized with Cuckoo search. Appl Soft Comput 27:602–609CrossRefGoogle Scholar
  29. 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
  30. Tai K, El-Sayed AR, Biglarbegian M, Gonzalez CI, Castillo O, Mahmud S (2016) Review of recent type-2 fuzzy controller applications. Algorithms 9(2):39MathSciNetCrossRefGoogle Scholar
  31. Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25:529–539MATHGoogle Scholar
  32. Ye J (2013) Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. Int J Gen Syst 42(4):386–394MathSciNetCrossRefMATHGoogle Scholar
  33. Ye J (2014a) A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. J Intell Fuzzy Syst 26(5):2459–2466MathSciNetMATHGoogle Scholar
  34. Ye J (2014b) Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making. J Intell Fuzzy Syst 26(1):165–172MATHGoogle Scholar
  35. Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353CrossRefMATHGoogle Scholar
  36. Zadeh L (1975) The concept of a linguistic variable and its application to approximate reasoning-1. Inf Sci 8:199–249MathSciNetCrossRefMATHGoogle Scholar
  37. Zavadskas EK, Bausys R, Kaklauskas A, Ubarte I, Kuzminske A, Gudiene N (2017) Sustainable market valuation of buildings by the single-valued neutrosophic MAMVA method. Appl Soft Comput 57:74–87CrossRefGoogle Scholar
  38. Zhang HY, Wang JQ, Chen XH (2014) Interval neutrosophic sets and their application in multicriteria decision-making problems. Sci World J 2014:15Google Scholar
  39. Zhang H, Wang J, Chen X (2016) An outranking approach for multi-criteria decision-making problems with interval-valued neutrosophic sets. Neural Comput Appl 27(3):615–627CrossRefGoogle Scholar
  40. Zimmermann HJ (2011) Fuzzy set theory—and its applications. Springer, New YorkGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial EngineeringYildiz Technical UniversityBesiktas, IstanbulTurkey
  2. 2.Department of Industrial Engineeringİstanbul Technical UniversityMacka, IstanbulTurkey

Personalised recommendations