Skip to main content
Log in

Multi-Attribute Decision-Making Method Based on Complex Interval-Valued q-Rung Orthopair Linguistic Heronian Mean Operators and Their Application

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

Abstract

In this manuscript, we firstly proposed a novel concept of complex interval-valued q-rung orthopair linguistic (CIVq-ROL) information by integrating the concepts of complex interval-valued q-rung orthopair fuzzy (CIVq-ROF) information and linguistic set (LS), and it is more generalized than most existing sets, and it is very feasible and valuable for depicting awkward and unreliable information in a difficult situation. Further, we developed some new operational laws of the CIVq-ROF information based on algebraic t-norm and t-conorm. It is also clear that it is a very challenging task to fuse the collection of data into a singleton set in information fusion and decision making, and the Heronian mean (HM) operator is an important aggregation operator, which can not only achieve the aggregation function from the collection of data into a singleton one, but also consider the relationship between any two data. Therefore, based on the CIVq-ROL information and HM operator, we proposed the CIVq-ROL Heronian mean (CIVq-ROLHM), CIVq-ROL weighted HM (CIVq-ROLWHM), CIVq-ROL geometric HM (CIVq-ROLGHM), and CIVq-ROL weighted geometric HM (CIVq-ROLWGHM) operators, and then, the various desirable properties and specific cases of them are also investigated. Furthermore, we developed a multi-attribute decision-making (MADM) procedure for evaluating the finest business in the country from the collection of four different types of electronics-related businesses based on the proposed operators. Finally, various examples are given to show the application of the invented techniques, and a comparative analysis is carried out for the parameters to show the advantages of the proposed approaches.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    MATH  Google Scholar 

  2. Mahmood, T.: A novel approach towards bipolar soft sets and their applications. J. Math. 2020, Article ID 4690808 (2020)

  3. Naveed, M., Riaz, M., Sultan, H., Ahmed, N.: Interval valued fuzzy soft sets and algorithm of IVFSS applied to the risk analysis of prostate cancer. Int. J. Comput. Appl. 975, 8887 (2020)

    Google Scholar 

  4. Chen, S.M., Tan, J.M.: Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets Syst. 67(2), 163–172 (1994)

    MathSciNet  MATH  Google Scholar 

  5. Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)

    MathSciNet  MATH  Google Scholar 

  6. Atanassov, K.T.: Interval valued intuitionistic fuzzy sets. In: Intuitionistic Fuzzy Sets, pp. 139–177. Physica, Heidelberg (1999)

  7. Ilbahar, E., Kahraman, C., Cebi, S.: Risk assessment of renewable energy investments: a modified failure mode and effect analysis based on prospect theory and intuitionistic fuzzy AHP. Energy 239, 121907 (2022)

    Google Scholar 

  8. Wang, W., Zhan, J., Mi, J.: A three-way decision approach with probabilistic dominance relations under intuitionistic fuzzy information. Inf. Sci. 582, 114–145 (2022)

    MathSciNet  Google Scholar 

  9. Pan, L., Deng, Y.: A novel similarity measure in intuitionistic fuzzy sets and its applications. Eng. Appl. Artif. Intell. 107, 104512 (2022)

    Google Scholar 

  10. Al-Qurashi, M., Shagari, M.S., Rashid, S., Hamed, Y.S., Mohamed, M.S.: Stability of intuitionistic fuzzy set-valued maps and solutions of integral inclusions. AIMS Math. 7(1), 315–333 (2022)

    MathSciNet  MATH  Google Scholar 

  11. Yager, R.R.: Pythagorean membership grades in multicriteria decision making. IEEE Trans. Fuzzy Syst. 22(4), 958–965 (2013)

    Google Scholar 

  12. Garg, H.: A novel accuracy function under interval-valued Pythagorean fuzzy environment for solving multicriteria decision making problem. J. Intell. Fuzzy Syst. 31(1), 529–540 (2016)

    MATH  Google Scholar 

  13. Tao, Z., Zhu, J., Zhou, L., Liu, J., Chen, H.: Multi-attribute decision making with Pythagorean fuzzy sets via conversions to intuitionistic fuzzy sets and ORESTE method. J. Control Decis. 8(3), 372–383 (2021)

    MathSciNet  Google Scholar 

  14. Naeem, K., Riaz, M., Afzal, D.: Pythagorean m-polar fuzzy sets and TOPSIS method for the selection of advertisement mode. J. Intell. Fuzzy Syst. 37(6), 8441–8458 (2019)

    Google Scholar 

  15. Riaz, M., Naeem, K., Afzal, D.: Pythagorean m-polar fuzzy soft sets with TOPSIS method for MCGDM. Punjab Univ. J. Math. 52(3), 21–46 (2020)

    MathSciNet  Google Scholar 

  16. Chen, T.Y.: New Chebyshev distance measures for Pythagorean fuzzy sets with applications to multiple criteria decision analysis using an extended ELECTRE approach. Expert Syst. Appl. 147, 113164 (2020)

    Google Scholar 

  17. Joshi, B.P., Singh, A., Bhatt, P.K., Vaisla, K.S.: Interval valued q-rung orthopair fuzzy sets and their properties. J. Intell. Fuzzy Syst. 35(5), 5225–5230 (2018)

    Google Scholar 

  18. Yager, R.R.: Generalized orthopair fuzzy sets. IEEE Trans. Fuzzy Syst. 25(5), 1222–1230 (2016)

    Google Scholar 

  19. Albahri, A.S., Albahri, O.S., Zaidan, A.A., Alnoor, A., Alsattar, H.A., Mohammed, R., et al.: Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: a distribution case study of COVID-19 vaccine doses. Comput. Stand. Interfaces 80, 103572 (2022)

    Google Scholar 

  20. Krishankumar, R., Nimmagadda, S.S., Rani, P., Mishra, A.R., Ravichandran, K.S., Gandomi, A.H.: Solving renewable energy source selection problems using a q-rung orthopair fuzzy-based integrated decision-making approach. J. Clean. Prod. 279, 123329 (2021)

    Google Scholar 

  21. Riaz, M., Sałabun, W., Athar Farid, H.M., Ali, N., Wątróbski, J.: A robust q-rung orthopair fuzzy information aggregation using Einstein operations with application to sustainable energy planning decision management. Energies 13(9), 2155 (2020)

    Google Scholar 

  22. Akram, M., Shumaiza, S.: Multi-criteria decision making based on q-rung orthopair fuzzy promethee approach. Iran. J. Fuzzy Syst. 18(5), 107–127 (2021)

    MathSciNet  MATH  Google Scholar 

  23. Ramot, D., Milo, R., Friedman, M., Kandel, A.: Complex fuzzy sets. IEEE Trans. Fuzzy Syst. 10(2), 171–186 (2002)

    Google Scholar 

  24. Al-Qudah, Y., Hassan, M., Hassan, N.: Fuzzy parameterized complex multi-fuzzy soft expert set theory and its application in decision-making. Symmetry 11(3), 358 (2019)

    MATH  Google Scholar 

  25. Liu, P., Ali, Z., Mahmood, T.: The distance measures and cross-entropy based on complex fuzzy sets and their application in decision making. J. Intell. Fuzzy Syst. 39(3), 3351–3374 (2020)

    Google Scholar 

  26. Alkouri, A.M.D.J.S., Salleh, A.R.: Complex intuitionistic fuzzy sets. In: AIP Conference Proceedings, vol. 1482(1), pp. 464–470. American Institute of Physics, College Park (2012)

  27. Garg, H., Rani, D.: Complex interval-valued intuitionistic fuzzy sets and their aggregation operators. Fund. Inform. 164(1), 61–101 (2019)

    MathSciNet  MATH  Google Scholar 

  28. Garg, H., Rani, D.: Some results on information measures for complex intuitionistic fuzzy sets. Int. J. Intell. Syst. 34(10), 2319–2363 (2019)

    Google Scholar 

  29. Garg, H., Rani, D.: A robust correlation coefficient measure of complex intuitionistic fuzzy sets and their applications in decision-making. Appl. Intell. 49(2), 496–512 (2019)

    Google Scholar 

  30. Garg, H., Rani, D.: Generalized geometric aggregation operators based on t-norm operations for complex intuitionistic fuzzy sets and their application to decision-making. Cogn. Comput. 4(1), 1–20 (2019)

    Google Scholar 

  31. Garg, H., Rani, D.: Robust averaging–geometric aggregation operators for complex intuitionistic fuzzy sets and their applications to MCDM process. Arab. J. Sci. Eng. 45(3), 2017–2033 (2020)

    Google Scholar 

  32. Garg, H., Rani, D.: Some generalized complex intuitionistic fuzzy aggregation operators and their application to multicriteria decision-making process. Arab. J. Sci. Eng. 44(3), 2679–2698 (2019)

    Google Scholar 

  33. Rani, D., Garg, H.: Complex intuitionistic fuzzy power aggregation operators and their applications in multicriteria decision-making. Expert. Syst. 35(6), e12325 (2018)

    Google Scholar 

  34. Garg, H., Rani, D.: New generalised Bonferroni mean aggregation operators of complex intuitionistic fuzzy information based on Archimedean t-norm and t-conorm. J. Exp. Theor. Artif. Intell. 32(1), 81–109 (2020)

    Google Scholar 

  35. Ullah, K., Mahmood, T., Ali, Z., Jan, N.: On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition. Complex Intell. Syst. 6(1), 15–27 (2020)

    Google Scholar 

  36. Ali, Z., Mahmood, T., Ullah, K., Khan, Q.: Einstein geometric aggregation operators using a novel complex interval-valued Pythagorean fuzzy setting with application in green supplier chain management. Rep. Mech. Eng. 2(1), 105–134 (2021)

    Google Scholar 

  37. Akram, M., Naz, S.: A novel decision-making approach under complex Pythagorean fuzzy environment. Math. Comput. Appl. 24(3), 73 (2019)

    MathSciNet  Google Scholar 

  38. Ma, X., Akram, M., Zahid, K., Alcantud, J.C.R.: Group decision-making framework using complex Pythagorean fuzzy information. Neural Comput. Appl. 33(6), 2085–2105 (2021)

    Google Scholar 

  39. Akram, M., Bashir, A., Samanta, S.: Complex pythagorean fuzzy planar graphs. Int. J. Appl. Comput. Math. 6(3), 1–27 (2020)

    MathSciNet  MATH  Google Scholar 

  40. Janani, K., Veerakumari, K.P., Vasanth, K., Rakkiyappan, R.: Complex Pythagorean fuzzy Einstein aggregation operators in selecting the best breed of Horsegram. Expert Syst. Appl. 187, 115990 (2022)

    Google Scholar 

  41. Akram, M., Garg, H., Zahid, K.: Extensions of ELECTRE-I and TOPSIS methods for group decision-making under complex Pythagorean fuzzy environment. Iran. J. Fuzzy Syst. 17(5), 147–164 (2020)

    Google Scholar 

  42. Mahmood, T., Ali, Z., Ullah, K., Khan, Q., AlSalman, H., Gumaei, A., Rahman, S.M.M.: Complex pythagorean fuzzy aggregation operators based on confidence levels and their applications. Math. Biosci. Eng. 19(1), 1078–1107 (2022)

    MATH  Google Scholar 

  43. Garg, H., Ali, Z., Mahmood, T.: Algorithms for complex interval-valued q-rung orthopair fuzzy sets in decision making based on aggregation operators, AHP, and TOPSIS. Expert. Syst. 38(1), e12609 (2021)

    Google Scholar 

  44. Ali, Z., Mahmood, T.: Maclaurin symmetric mean operators and their applications in the environment of complex q-rung orthopair fuzzy sets. Comput. Appl. Math. 39, 1–27 (2020)

    MathSciNet  MATH  Google Scholar 

  45. Garg, H., Naz, S., Ziaa, F., Shoukat, Z.: A ranking method based on Muirhead mean operator for group decision making with complex interval-valued q-rung orthopair fuzzy numbers. Soft Comput. 25(22), 14001–14027 (2021)

    MATH  Google Scholar 

  46. Liu, H.Z., Pei, D.W.: HOWA operator and its application to multi-attribute decision making. J. Zhejiang Sci. Tech. Univ. 25, 138–142 (2012)

    Google Scholar 

  47. Wei, G., Wang, X.: Some geometric aggregation operators based on interval-valued intuitionistic fuzzy sets and their application to group decision making. In: 2007 International Conference on Computational Intelligence and Security (CIS 2007), pp. 495–499. IEEE (2007)

  48. Xu, Z., Chen, J.: On geometric aggregation over interval-valued intuitionistic fuzzy information. In: Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), vol. 2, pp. 466–471. IEEE (2007)

  49. Chen, T.Y., Wang, H.P., Lu, Y.Y.: A multicriteria group decision-making approach based on interval-valued intuitionistic fuzzy sets: a comparative perspective. Expert Syst. Appl. 38(6), 7647–7658 (2011)

    Google Scholar 

  50. Xu, Z.S.: Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control Decis. 2, 019 (2007)

    Google Scholar 

  51. Ye, J.: Fuzzy cross entropy of interval-valued intuitionistic fuzzy sets and its optimal decision-making method based on the weights of alternatives. Expert Syst. Appl. 38(5), 6179–6183 (2011)

    Google Scholar 

  52. Chen, T.Y.: A prioritized aggregation operator-based approach to multiple criteria decision making using interval-valued intuitionistic fuzzy sets: a comparative perspective. Inf. Sci. 281, 97–112 (2014)

    MathSciNet  MATH  Google Scholar 

  53. Ye, J.: Multicriteria fuzzy decision-making method based on a novel accuracy function under interval-valued intuitionistic fuzzy environment. Expert Syst. Appl. 36(3), 6899–6902 (2009)

    Google Scholar 

  54. Şahin, R.: Fuzzy multicriteria decision making method based on the improved accuracy function for interval-valued intuitionistic fuzzy sets. Soft. Comput. 20(7), 2557–2563 (2016)

    MATH  Google Scholar 

  55. Peng, X., Yang, Y.: Fundamental properties of interval-valued Pythagorean fuzzy aggregation operators. Int. J. Intell. Syst. 31(5), 444–487 (2016)

    Google Scholar 

  56. Liang, D., Darko, A.P., Xu, Z.: Interval-valued Pythagorean fuzzy extended Bonferroni mean for dealing with heterogenous relationship among attributes. Int. J. Intell. Syst. 33(7), 1381–1411 (2018)

    Google Scholar 

  57. Rahman, K., Abdullah, S., Shakeel, M., Ali Khan, M.S., Ullah, M.: Interval-valued Pythagorean fuzzy geometric aggregation operators and their application to group decision making problem. Cogent Math. 4(1), 1338638 (2017)

    MathSciNet  MATH  Google Scholar 

  58. Rahman, K., Abdullah, S.: Generalized interval-valued Pythagorean fuzzy aggregation operators and their application to group decision-making. Granular Comput. 4(1), 15–25 (2019)

    MATH  Google Scholar 

  59. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975)

    MathSciNet  MATH  Google Scholar 

  60. Wang, J.Q., Li, H.B.: Multi-criteria decision-making method based on aggregation operators for intuitionistic linguistic fuzzy numbers. Control Decis. 25(10), 1571–1574 (2010)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peide Liu.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, X., Ali, Z., Mahmood, T. et al. Multi-Attribute Decision-Making Method Based on Complex Interval-Valued q-Rung Orthopair Linguistic Heronian Mean Operators and Their Application. Int. J. Fuzzy Syst. 25, 1338–1359 (2023). https://doi.org/10.1007/s40815-022-01455-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-022-01455-0

Keywords

Navigation