Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Multiple Attribute Decision-Making Methods Based on the Expected Value and the Similarity Measure of Hesitant Neutrosophic Linguistic Numbers

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

The existing neutrosophic linguistic decision-making approach uses only one neutrosophic linguistic number (NLN) to express its evaluation value of an attribute in decision making. Sometimes, it may not reflect exactly what decision makers mean due to the ambiguity and indeterminacy of their cognitions to complex decision-making problems. In this situation, decision makers might hesitate among several NLNs to express their opinions. To deal with the issue, this paper defines hesitant neutrosophic linguistic numbers (HNLNs), the expected value of HNLN and proposes the generalized distance and similarity measure between two HNLN sets based on the least common multiple cardinality for HNLNs. Then, multiple attribute decision-making (MADM) methods are established based on the expected value and the similarity measure under a HNLN environment. In the proposed decision-making methods, the evaluation values of alternatives over attributes provided by decision makers are HNLNs, and then all the alternatives are ranked by the expected values of HNLNs and the similarity measure values between each alternative and the ideal alternative (ideal solution) to select the best one. An actual example on the selection problem of manufacturing alternatives is provided to demonstrate the applicability of the developed decision-making approaches. The decision results of manufacturing alternatives and the comparative analysis indicate that the proposed methods are effective and superior to existing ones. The MADM methods based on the expected value and the similarity measure can effectively deal with MADM problems with HNLN information and are more objective and more useful than the existing ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning part I. Inf Sci. 1975;8(2):199–249. https://doi.org/10.1016/0020-0255(75)90036-5.

    Article  Google Scholar 

  2. Herrera F, Herrera-Viedma E, Verdegay JL. A sequential selection process in group decision making with a linguistic assessment approach. Inf Sci. 1995;85(2):223–39. https://doi.org/10.1016/0020-0255(95)00025-K.

    Article  Google Scholar 

  3. Herrera F, Herrera-Viedma E, Verdegay JL. A model of consensus in group decision making under linguistic assessments. Fuzzy Sets Syst. 1996;79(1):73–87.

    Article  Google Scholar 

  4. Herrera F, Herrera-Viedma E. Aggregation operators for linguistic weighted information, IEEE transactions on systems. Man Cybern A. 1997;27(5):646–55. https://doi.org/10.1109/3468.618263.

    Article  Google Scholar 

  5. Herrera F, Herrera-Viedma E. Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets Syst. 2000;115(1):67–82. https://doi.org/10.1016/S0165-0114(99)00024-X.

    Article  Google Scholar 

  6. Xu ZS. EOWA and EOWG operators for aggregating linguistic labels based on linguistic preference relations. Int J Uncertain, Fuzziness Knowl-Based Syst. 2004;12(6):791–810. https://doi.org/10.1142/S0218488504003211.

    Article  Google Scholar 

  7. Xu ZS. A note on linguistic hybrid arithmetic averaging operator in multiple attribute group decision making with linguistic information. Group Decis Negot. 2006;15(6):593–604. https://doi.org/10.1007/s10726-005-9008-4.

    Article  Google Scholar 

  8. Xu ZS. Goal programming models for multiple attribute decision making under linguistic setting. J Manag Sci China. 2006;9(2):9–17.

    Google Scholar 

  9. Meng F, Wang C, Chen X. Linguistic interval hesitant fuzzy sets and their application in decision making. Cogn Comput. 2016;8(1):52–68. https://doi.org/10.1007/s12559-015-9340-1.

    Article  Google Scholar 

  10. Tian Z, Wang J, Wang J, Zhang H. A likelihood-based qualitative flexible approach with hesitant fuzzy linguistic information. Cogn Comput. 2016;8(4):670–83. https://doi.org/10.1007/s12559-016-9400-1.

    Article  CAS  Google Scholar 

  11. Liu P, Tang G. Multi-criteria group decision-making based on interval neutrosophic uncertain linguistic variables and Choquet integral. Cogn Comput. 2016;8(6):1036–56. https://doi.org/10.1007/s12559-016-9428-2.

    Article  Google Scholar 

  12. Rodríguez RM, Martínez L, Herrera F. Hesitant fuzzy linguistic terms sets for decision making. IEEE Trans Fuzzy Syst. 2012;20(1):109–19. https://doi.org/10.1109/TFUZZ.2011.2170076.

    Article  Google Scholar 

  13. Rodríguez RM, Martínez L, Herrera F. A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term set. Inf Sci. 2013;241:28–42. https://doi.org/10.1016/j.ins.2013.04.006.

    Article  Google Scholar 

  14. Liao HC, Xu ZS, Zeng XJ. Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Inf Sci. 2014;271:125–42. https://doi.org/10.1016/j.ins.2014.02.125.

    Article  Google Scholar 

  15. Wei CP, Zhao N, Tang XJ. Operators and comparisons of hesitant fuzzy linguistic term sets. IEEE Trans Fuzzy Syst. 2014;22(3):575–85. https://doi.org/10.1109/TFUZZ.2013.2269144.

    Article  Google Scholar 

  16. Liao HC, Xu ZS. Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for HFLTSs and their application in qualitative decision making. Expert Syst Appl. 2015;42(12):5328–36. https://doi.org/10.1016/j.eswa.2015.02.017.

    Article  Google Scholar 

  17. Liao HC, Xu ZS, Zeng XJ, Merigó JM. Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowl-Based Syst. 2015;76:127–38. https://doi.org/10.1016/j.knosys.2014.12.009.

    Article  Google Scholar 

  18. Xu ZS. Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment. Inf Sci. 2004;168(1-4):171–84. https://doi.org/10.1016/j.ins.2004.02.003.

    Article  Google Scholar 

  19. Xu ZS. Induced uncertain linguistic OWA operators applied to group decision making. Inform Fusion. 2006c;7(2):231–8. https://doi.org/10.1016/j.inffus.2004.06.005.

    Article  Google Scholar 

  20. Wei GW, Zhao XF, Lin R, Wang HJ. Uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making. Appl Math Model. 2013;37(7):5277–85. https://doi.org/10.1016/j.apm.2012.10.048.

    Article  Google Scholar 

  21. Smarandache F. Neutrosophy: neutrosophic probability, set, and logic. Rehoboth: American Research Press; 1998.

    Google Scholar 

  22. Smarandache F. Introduction to neutrosophic measure, neutrosophic integral, and neutrosophic probability. Craiova–Columbus: Sitech & Education Publisher; 2013.

    Google Scholar 

  23. Smarandache F. Introduction to neutrosophic statistics. Craiova: Sitech & Education Publishing; 2014.

    Google Scholar 

  24. Ye J. Bidirectional projection method for multiple attribute group decision making with neutrosophic numbers. Neural Comput & Applic. 2017;28(5):1021–9. https://doi.org/10.1007/s00521-015-2123-5.

    Article  Google Scholar 

  25. Ye J. Multiple-attribute group decision-making method under a neutrosophic number environment. J Intell Syst. 2016;25(3):377–86.

    Google Scholar 

  26. Kong L, Wu Y, Ye J. Misfire fault diagnosis method of gasoline engines using the cosine similarity measure of neutrosophic numbers. Neutrosophic Sets Syst. 2015;8:43–6.

    Google Scholar 

  27. Ye J. Fault diagnoses of steam turbine using the exponential similarity measure of neutrosophic numbers. J Intell Fuzzy Syst. 2016;30(4):1927–34. https://doi.org/10.3233/IFS-151903.

    Article  Google Scholar 

  28. Smarandache F. Symbolic neutrosophic theory. Bruxelles: EuropaNova asbl; 2015.

    Google Scholar 

  29. Ye J. Aggregation operators of neutrosophic linguistic numbers for multiple attribute group decision making. SpringerPlus. 2016;5(1691):11. https://doi.org/10.1186/s40064-016-3247-5.

    Article  Google Scholar 

  30. Torra V, Narukawa Y. On hesitant fuzzy sets and decision. In: Proceedings of the 18th IEEE International Conference on Fuzzy Systems. Jeju Island, Korea; 2009; 1378–1382. https://doi.org/10.1109/FUZZY.2009.5276884, http://ieeexplore.ieee.org/document/5276884/

  31. Torra V. Hesitant fuzzy sets. Int J Intell Syst. 2010;25:529–39.

    Google Scholar 

  32. Smarandache F. N-valued refined neutrosophic logic and its applications in physics. Prog Phys. 2013;4:143–6.

    Google Scholar 

  33. Smarandache F. Symbolic Neutrosophic Logic. Bruxelles: Europa Nova; 2015.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jun Ye.

Ethics declarations

Conflicts of Interest

The author declares that he has no conflict of interest.

Human and Animal Rights

This article does not contain any studies with human participants or animals performed by the author.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, J. Multiple Attribute Decision-Making Methods Based on the Expected Value and the Similarity Measure of Hesitant Neutrosophic Linguistic Numbers. Cogn Comput 10, 454–463 (2018). https://doi.org/10.1007/s12559-017-9535-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-017-9535-8

Keywords