Skip to main content

Advertisement

Springer Nature Link
Log in

An ORESTE approach for multi-criteria decision-making with probabilistic hesitant fuzzy information

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

As an important extension of fuzzy number, the probabilistic hesitant fuzzy element (PHFE) shows the flexibility of decision makers in expressing hesitant information in multi-criteria decision-making (MCDM) processes. Accordingly, numerous research findings have been obtained since PHFE introduction. However, a few important issues in PHFE utilization remain to be addressed. This study introduces the French organization Rangement Et Synthese De Ronnees Relationnelles’ (ORESTE) approach for MCDM with probabilistic hesitant fuzzy information. First, the limitations of normalized PHFE (NPHFE), Euclidean distance, and several operations in previous studies are discussed. Subsequently, an algorithm is designed to derive the new NPHFE. A new Euclidean distance and several operations are developed on the basis of the proposed NPHFE. Second, the ORESTE approach is extended to probabilistic hesitant fuzzy environments. Lastly, the problem of selecting best research topic is presented to demonstrate that the proposed approach is effective. A comparative study with other approaches is conducted with identical illustrative example.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Explore related subjects

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

References

  1. Yu SM, Wang J, Wang JQ, Li L (2018) A multi-criteria decision-making model for hotel selection with linguistic distribution assessments. Appl Soft Comput 67:741–755

    Article  Google Scholar 

  2. Cheng SH (2018) Autocratic decision making using group recommendations based on hesitant fuzzy sets for green hotels selection and bidders selection. Inf Sci 467:604–617

    Article  MathSciNet  Google Scholar 

  3. Zhang XY, Wang XK, Yu SM, Wang JQ, Wang TL (2018) Location selection of offshore wind power station by consensus decision framework using picture fuzzy modelling. J Clean Prod 202:980–992

    Article  Google Scholar 

  4. Nie RX, Tian ZP, Wang JQ, Zhang HY, Wang TL (2018) Water security sustainability evaluation: applying a multistage decision support framework in industrial region. J Clean Prod 196:1681–1704

    Article  Google Scholar 

  5. Zhao R, Liu SL, Liu YY, Zhang LP, Li YP (2018) A safety vulnerability assessment for chemical enterprises: a hybrid of a data envelopment analysis and fuzzy decision-making. J Loss Prev Process Ind 56:95–103

    Article  Google Scholar 

  6. Wu HY, Xu ZS, Ren PJ, Liao HC (2018) Hesitant fuzzy linguistic projection model to multi-criteria decision making for hospital decision support systems. Comput Ind Eng 115:449–458

    Article  Google Scholar 

  7. Ren JZ, Liang HW (2017) Measuring the sustainability of marine fuels: a fuzzy group multi-criteria decision making approach. Transp Res Part D Transp Environ 54:12–29

    Article  Google Scholar 

  8. Liu YJ, Zhang WG, Gupta P (2018) International asset allocation optimization with fuzzy return. Knowl Based Syst 139:189–199

    Article  Google Scholar 

  9. Aggarwal M (2019) Hesitant information sets and application in group decision making. Appl Soft Comput 75:120–129

    Article  Google Scholar 

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

    MATH  Google Scholar 

  11. Zhou W, Xu ZS (2018) Probability calculation and element optimization of probabilistic hesitant fuzzy preference relations based on expected consistency. PP, IEEE Trans Fuzzy Syst, pp 1367–1378

    Google Scholar 

  12. Li J, Wang JQ, Hu JH (2019) Consensus building for hesitant fuzzy preference relations with multiplicative consistency. Comput Ind Eng 128:387–400

    Article  Google Scholar 

  13. Dinçer H, Yüksel S, Martínez L (2019) Analysis of balanced scorecard-based SERVQUAL criteria based on hesitant decision-making approaches. Comput Ind Eng 131:1–12

    Article  Google Scholar 

  14. Hussain Z, Yang MS (2018) Entropy for hesitant fuzzy sets based on Hausdorff metric with construction of hesitant fuzzy TOPSIS. Int J Fuzzy Syst 20:2517–2533

    Article  MathSciNet  Google Scholar 

  15. Xu ZS, Zhang S (2019) An overview on the applications of the hesitant fuzzy sets in group decision-making: theory, support and methods. Front Eng Manag 6:163–182

    Article  Google Scholar 

  16. Zhu B. Decision method for research and application based on preference relation., Retrieved from China Dissertation Database

  17. Li J, Wang ZX (2019) Multi-attribute decision-making based on prioritized operators under probabilistic hesitant fuzzy environments. Soft Comput 23:3853–3868

    Article  MATH  Google Scholar 

  18. Xu ZS, Zhou W (2017) Consensus building with a group of decision makers under the hesitant probabilistic fuzzy environment. Fuzzy Optim Decis Making 16:481–503

    Article  MathSciNet  MATH  Google Scholar 

  19. Zhang S, Xu ZS, He Y (2017) Operations and integrations of probabilistic hesitant fuzzy information in decision making. Inf Fus 38:1–11

    Article  Google Scholar 

  20. Hao ZN, Xu ZS, Zhao H, Su Z (2017) Probabilistic dual hesitant fuzzy set and its application in risk evaluation. Knowl Based Syst 127:16–28

    Article  Google Scholar 

  21. Jiang FJ, Ma QG (2018) Multi-attribute group decision making under probabilistic hesitant fuzzy environment with application to evaluate the transformation efficiency. Appl Intell 48:953–965

    Article  Google Scholar 

  22. Zhang S, Xu ZS, Wu HY (2019) Fusions and preference relations based on probabilistic interval-valued hesitant fuzzy information in group decision making. Soft Comput 23:8291–8306

    Article  MATH  Google Scholar 

  23. Ding J, Xu ZS, Zhao N (2017) An interactive approach to probabilistic hesitant fuzzy multi-attribute group decision making with incomplete weight information. J Intell Fuzzy Syst 32:2523–2536

    Article  MATH  Google Scholar 

  24. He Y, Xu ZS (2019) Multi-attribute decision making methods based on reference ideal theory with probabilistic hesitant information. Expert Syst Appl 118:459–469

    Article  Google Scholar 

  25. Tian XL, Xu ZS, Fujita H (2018) Sequential funding the venture project or not? A prospect consensus process with probabilistic hesitant fuzzy preference information. Knowl Based Syst 161:172–184

    Article  Google Scholar 

  26. Song CY, Xu ZS, Zhao H (2019) New correlation coefficients between probabilistic hesitant fuzzy sets and their applications in cluster analysis. Int J Fuzzy Syst 21:355–368

    Article  MathSciNet  Google Scholar 

  27. Xu ZS, He Y, Wang XZ (2019) An overview of probabilistic-based expressions for qualitative decision-making: techniques, comparisons and developments. Int J Mach Learn Cybern 10:1513–1528

    Article  Google Scholar 

  28. Li J, Wang JQ (2017) An extended QUALIFLEX method under probability hesitant fuzzy environment for selecting green suppliers. Int J Fuzzy Syst 19:1866–1879

    Article  MathSciNet  Google Scholar 

  29. Li J, Wang JQ (2017) Multi-criteria outranking methods with hesitant probabilistic fuzzy sets. Cogn Comput 9:611–625

    Article  Google Scholar 

  30. Li J, Wang JQ, Hu JH (2019) Multi-criteria decision-making method based on dominance degree and BWM with probabilistic hesitant fuzzy information. Int J Mach Learn Cybern 10:1671–1685

    Article  Google Scholar 

  31. Zhou W, Xu ZS (2017) Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Appl Soft Comput 60:297–311

    Article  Google Scholar 

  32. Krishankumar R, Ravichandran KS, Kar S, Gupta P, Mehlawat MK (2019) Interval-valued probabilistic hesitant fuzzy set for multi-criteria group decision-making. Soft Comput 23:10853–10879

    Article  Google Scholar 

  33. Roubens M (1982) Preference relations an actions and criteria in multicriteria decision making. Eur J Oper Res 10:51–55

    Article  MATH  Google Scholar 

  34. Liao HC, Wu XL, Liang XD, Yang JB, Xu DL, Herrera F (2018) A continuous interval-valued linguistic ORESTE method for multi-criteria group decision making. Knowl Based Syst 153:65–77

    Article  Google Scholar 

  35. Liao HC, Wu XL, Liang XD, Xu JP, Herrera F (2018) A new hesitant fuzzy linguistic ORESTE method for hybrid multi-criteria decision making. IEEE Trans Fuzzy Syst 26:3793–3807

    Article  Google Scholar 

  36. Wu XL, Liao HC (2018) An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making. Inf Fus 43:13–26

    Article  Google Scholar 

  37. Tian ZP, Nie RX, Wang JQ, Zhang HY (2019) Signed distance-based ORESTE for multi-criteria group decision-making with multi-granular unbalanced hesitant fuzzy linguistic information. Expert Syst 36:e12350

    Article  Google Scholar 

  38. Wang XD, Gou XJ, Xu ZS (2020) Assessment of traffic congestion with ORESTE method under double hierarchy hesitant fuzzy linguistic environment. Appl Soft Comput 86:105864

    Article  Google Scholar 

  39. Xia MM, Xu ZS (2011) Hesitant fuzzy information aggregation in decision making. Int J Approx Reason 52:395–407

    Article  MathSciNet  MATH  Google Scholar 

  40. Wu XL, Liao HC, Xu ZS, Hafezalkotob A, Herrera F (2018) Probabilistic linguistic MULTIMOORA: a multi-criteria decision making method based on the probabilistic linguistic expectation function and the improved borda rule. IEEE Trans Fuzzy Syst 26:3688–3702

    Article  Google Scholar 

  41. Xu YJ, Cabrerizo FJ, Herrera-Viedma E (2017) A consensus model for hesitant fuzzy preference relations and its application in water allocation management. Appl Soft Comput 58:265–284

    Article  Google Scholar 

  42. Wu ZB, Xu JP (2018) A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters. Inf Fus 41:217–231

    Article  Google Scholar 

  43. Mu ZM, Zeng SZ, Baležentis T (2015) A novel aggregation principle for hesitant fuzzy elements. Knowl-Based Syst 84:134–143

    Article  Google Scholar 

  44. Xu ZS, Cai XQ (2010) Recent advances in intuitionistic fuzzy information aggregation. Fuzzy Optim Decis Making 9:359–381

    Article  MathSciNet  MATH  Google Scholar 

  45. Xu ZS, Zhang XL (2013) Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowl Based Syst 52:53–64

    Article  Google Scholar 

  46. Pastijn H, Leysen J (1989) Constructing an outranking relation with ORESTE. Math Comput Model 12:1255–1268

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the anonymous reviewers and the editor for their insightful and constructive comments and suggestions that have led to an improved version of this paper. This work was supported by the National Natural Science Foundation of China (nos.61866006), Guangxi innovation-driven development of special funds project (gui ke AA17204091), the Natural Science Foundation of Guangxi (nos. AB17292095), the Research Funds for the Guangxi University Xingjian College of Science and Liberal Arts (nos. Y2018ZKT01) and Promotion project of Middle-aged and Young Teachers’ Basic Scientific Research Ability in Universities of Guangxi (no. 2019KY0963).

Author information

Authors and Affiliations

  1. School of Logistics Management and Engineering, Nanning Normal University, Nanning, 530001, People’s Republic of China

    Jian Li & Qiongxia Chen

  2. School of Xingjian College of Science and Liberal Arts, Guangxi University, Nanning, 530005, People’s Republic of China

    Jian Li & Li-li Niu

  3. School of Mathematics and Information Science, Guangxi University, Nanning, 530004, People’s Republic of China

    Zhong-xing Wang

Authors
  1. Jian Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Qiongxia Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Li-li Niu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhong-xing Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Qiongxia Chen.

Ethics declarations

Conflict of interest

Author Jian Li, Qiongxia Chen, Li-li Niu and Zhong-xing Wang declare that they have no conflict of interest.

Ethical approval

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

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Chen, Q., Niu, Ll. et al. An ORESTE approach for multi-criteria decision-making with probabilistic hesitant fuzzy information. Int. J. Mach. Learn. & Cyber. 11, 1591–1609 (2020). https://doi.org/10.1007/s13042-020-01060-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01060-3

Keywords