Skip to main content
SpringerLink
Log in

A Framework for Multi-Attribute Group Decision-Making Using Double Hierarchy Hesitant Fuzzy Linguistic Term Set

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

As a generalization to hesitant fuzzy linguistic term set (HFLTS), double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is presented which circumvents the weakness of HFLTS in representing complex linguistic terms. DHHFLTS has two linguistic hierarchies with the second hierarchy supplementing the primary which enables decision-makers (DMs) to represent complex linguistic terms better. Motivated by the power of DHHFLTS, in this paper, a new decision framework is presented under DHHFLTS context. Initially, a new aggregation operator called double hierarchy hesitant fuzzy hybrid aggregation (DHHFHA) operator is proposed for sensible aggregation of DMs’ preference information. Further, weights of attributes are calculated by extending statistical variance (SV) method under DHHFLTS context. Objects are prioritized by extending the popular WASPAS (weighted aggregated sum product assessment) method to DHHFLTS context. The applicability and usefulness of the proposed framework are realized by demonstrating a risk management technique (RMT) selection problem for a construction project. Finally, the superiority and weakness of the proposed framework are discussed by comparison with other methods.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Triantaphyllou, E., Shu, B.: Multi-criteria decision making: an operations research approach. Encycl. Electr. Electron. Eng. 15, 175–186 (1998)

    Google Scholar 

  2. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Inf. Sci. 8, 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  3. Herrera, F., Herrera-Viedma, E.: Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets Syst. 115, 67–82 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  4. Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: Linguistic measures based on fuzzy coincidence for reaching consensus in group decision making. Int. J. Approx. Reason. 16, 309–334 (1997)

    Article  MATH  Google Scholar 

  5. Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A rational consensus model in group decision making using linguistic assessments. Fuzzy Sets Syst. 88, 31–49 (1997)

    Article  MATH  Google Scholar 

  6. Rodriguez, R.M., Martinez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20, 109–119 (2012)

    Article  Google Scholar 

  7. Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A sequential selection process in group decision making with a linguistic assessment approach. Inf. Sci. 239, 223–239 (1995)

    Article  MATH  Google Scholar 

  8. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25, 529–539 (2010)

    MATH  Google Scholar 

  9. Liao, H., Xu, Z., Herrera-Viedma, E., Herrera, F.: Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey. Int. J. Fuzzy Syst. 20, 2084–2110 (2017)

    Article  MathSciNet  Google Scholar 

  10. Gou, X., Liao, H., Xu, Z., Herrera, F.: Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: a case of study to evaluate the implementation status of haze controlling measures. Inf. Fusion. 38, 22–34 (2017)

    Article  Google Scholar 

  11. Pang, Q., Wang, H., Xu, Z.: Probabilistic linguistic term sets in multi-attribute group decision making. Inf. Sci. 369, 128–143 (2016)

    Article  Google Scholar 

  12. Wang, J.H., Hao, J.: A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 14, 435–445 (2006)

    Article  Google Scholar 

  13. Tang, Y., Zheng, J.: Linguistic modelling based on semantic similarity relation among linguistic labels. Fuzzy Sets Syst. 157, 1662–1673 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Meng, F., Chen, X., Zhang, Q.: Multi-attribute decision analysis under a linguistic hesitant fuzzy environment. Inf. Sci. 267, 287–305 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. Xu, Z.: Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment. Inf. Sci. 168, 171–184 (2004)

    Article  MATH  Google Scholar 

  16. Wang, H., Xu, Z., Zeng, X.J.: Modeling complex linguistic expressions in qualitative decision making: an overview. Knowledge-Based Syst. 144, 174–187 (2018)

    Article  Google Scholar 

  17. Rodríguez, R.M., Labella, A., Martínez, L.: An overview on fuzzy modelling of complex linguistic preferences in decision making. Int. J. Comput. Intell. Syst. 9, 81–94 (2016)

    Article  Google Scholar 

  18. Montserrat-Adell, J., Xu, Z., Gou, X., Agell, N.: Free double hierarchy hesitant fuzzy linguistic term sets: an application on ranking alternatives in GDM. Inf. Fusion. 47, 45–59 (2018)

    Article  Google Scholar 

  19. Gou, X., Xu, Z., Herrera, F.: Consensus reaching process for large-scale group decision making with double hierarchy hesitant fuzzy linguistic preference relations. Knowledge-Based Syst. 157, 20–33 (2018)

    Article  Google Scholar 

  20. Muirhead, R.F.: Some methods applicable to identities and inequalities of symmetric algebraic functions of n letters. Proc. Edinburgh Math. Soc. 21, 144–162 (1902)

    Article  Google Scholar 

  21. Liu, P., Teng, F.: Some Muirhead mean operators for probabilistic linguistic term sets and their applications to multiple attribute decision-making. Appl. Soft Comput. 68, 396–431 (2018). https://doi.org/10.1016/j.asoc.2018.03.027

    Article  Google Scholar 

  22. Kao, C.: Weight determination for consistently ranking alternatives in multiple criteria decision analysis. Appl. Math. Model. 34, 1779–1787 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  23. Rao, R.V., Patel, B.K., Parnichkun, M.: Industrial robot selection using a novel decision making method considering objective and subjective preferences. Rob. Auton. Syst. 59, 367–375 (2011)

    Article  Google Scholar 

  24. Mardani, A., Nilashi, M., Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M.Z.M., et al.: A systematic review and meta-analysis of SWARA and WASPAS methods: theory and applications with recent fuzzy developments. Appl. Soft Comput. 57, 265–292 (2017)

    Article  Google Scholar 

  25. Singh, S., Olugu, E.U., Musa, S.N., Mahat, A.B., Wong, K.Y.: Strategy selection for sustainable manufacturing with integrated AHP-VIKOR method under interval-valued fuzzy environment. Int. J. Adv. Manuf. Technol. 84, 547–563 (2016)

    Article  Google Scholar 

  26. Büyüközkan, G., Görener, A.: evaluation of product development partners using an integrated AHP-VIKOR model. Kybernetes 44, 220–237 (2015)

    Article  Google Scholar 

  27. Zhang, Y., Wang, Y., Wang, J.: Objective attributes weights determining based on shannon information entropy in hesitant fuzzy multiple attribute decision making. Math. Probl. Eng. 2014, 1–7 (2014)

    Google Scholar 

  28. Hafezalkotob, A., Hafezalkotob, A.: Fuzzy entropy-weighted MULTIMOORA method for materials selection. J. Intell. Fuzzy Syst. 31, 1211–1226 (2016). https://doi.org/10.3233/IFS-162186

    Article  MATH  Google Scholar 

  29. Chýna, V., Kuncová, M., Sekni, J.: Estimation of weights in multi-criteria decision-making optimization models. In: Proceedings of 30th International Conference Mathematical Methods in Economics, 2013: pp. 355–360

  30. Xu, Z., Zhang, X.: Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowledge-Based Syst. 52, 53–64 (2013)

    Article  Google Scholar 

  31. Liu, S., Chan, F.T.S., Ran, W.: Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst. Appl. 55, 37–47 (2016)

    Article  Google Scholar 

  32. Chakraborty, S., Zavadskas, E.K.: Applications of WASPAS method in manufacturing decision making. Informatica. 25, 1–20 (2014)

    Article  Google Scholar 

  33. Chatterjee, K., Zavadskas, E.K., Tamošaitiene, J., Adhikary, K., Kar, S.: A hybrid MCDM technique for risk management in construction projects. Symmetry. 10, 1–31 (2018)

    Article  Google Scholar 

  34. Lyons, T., Skitmore, M.: Project risk management in the Queensland engineering construction industry: a survey. Int. J. Proj. Manag. 22, 51–61 (2004)

    Article  Google Scholar 

  35. Lima Jr., F.R., Osiro, L., Carpinetti, L.C.R.: A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Appl. Soft Comput. 21, 194–209 (2014)

    Article  Google Scholar 

  36. Gou, X., Xu, Z., Liao, H., Herrera, F.: Multiple criteria decision making based on distance and similarity measures under double hierarchy hesitant fuzzy linguistic environment. Comput. Ind. Eng. 126, 516–530 (2018)

    Article  Google Scholar 

  37. Saaty, T.L., Ozdemir, M.S.: Why the magic number seven plus or minus two. Math. Comput. Model. 38, 233–244 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  38. Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15, 72–101 (1904)

    Article  Google Scholar 

  39. Chen, J., Li, S., Ma, S., Wang, X.: M-Polar fuzzy sets: an extension of bipolar fuzzy sets. Sci. World J. 2014, 1–8 (2014)

    Google Scholar 

  40. Deng, S., Liu, J., Wang, X.: The properties of fuzzy tensor and its application in multiple attribute group decision making. IEEE Trans. Fuzzy Syst. (2018). https://doi.org/10.1109/TFUZZ.2018.2865923

    Google Scholar 

  41. Yager, R.R.: Generalized orthopair fuzzy sets. IEEE Trans. Fuzzy Syst. 25, 1222–1230 (2017)

    Article  Google Scholar 

Download references

Acknowledgement

Authors dedicate their earnest gratitude to the editor and the anonymous reviewers for their valuable comments that helped us improve the quality of the paper.

Funding

Authors thank University Grants Commission (UGC), India, and Department of Science & Technology (DST), India, for their financial support from Grant Nos. F./2015-17/RGNF-2015-17-TAM-83 and SR/FST/ETI-349/2013.

Author information

Authors and Affiliations

  1. School of Computing, SASTRA University, Thanjavur, 613401, TN, India

    R. Krishankumar, L. S. Subrajaa & K. S. Ravichandran

  2. Department of Mathematics, National Institute of Technology, Durgapur, 713209, West Bengal, India

    Samarjit Kar

  3. Department of Pure Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

    Arsham Borumand Saeid

Authors
  1. R. Krishankumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. S. Subrajaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. S. Ravichandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samarjit Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arsham Borumand Saeid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samarjit Kar.

Ethics declarations

Conflict of interest

All authors of this research paper declare that there is no conflict of interest.

Ethical approval

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

Informed consent

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Krishankumar, R., Subrajaa, L.S., Ravichandran, K.S. et al. A Framework for Multi-Attribute Group Decision-Making Using Double Hierarchy Hesitant Fuzzy Linguistic Term Set. Int. J. Fuzzy Syst. 21, 1130–1143 (2019). https://doi.org/10.1007/s40815-019-00618-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-019-00618-w

Keywords

Search

Navigation