#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers for multiple attribute group decision making


Autoři: Yanru Zhong aff001;  Hong Gao aff001;  Xiuyan Guo aff001;  Yuchu Qin aff002;  Meifa Huang aff003;  Xiaonan Luo aff001
Působiště autorů: Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, Guilin, PR China aff001;  School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom aff002;  School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, PR China aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222007

Souhrn

In this paper, a set of Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers (qROFNs) are presented, and a multiple attribute group decision making (MAGDM) method based on these operators is proposed. First, the operational rules of qROFNs based on the Dombi t-conorm and t-norm are introduced. A q-rung orthopair fuzzy Dombi partitioned Heronian mean (qROFDPHM) operator and its weighted form are then established in accordance with these rules. To reduce the negative effect of unreasonable attribute values on the aggregation results of these operators, a q-rung orthopair fuzzy Dombi power partitioned Heronian mean operator and its weighted form are constructed by combining qROFDPHM operator with the power average operator. A method to solve MAGDM problems based on qROFNs and the constructed operators is designed. Finally, a practical example is described, and experiments and comparisons are performed to demonstrate the feasibility and effectiveness of the proposed method. The demonstration results show that the method is feasible, effective, and flexible; has satisfying expressiveness; and can consider all the interrelationships among different attributes and reduce the negative influence of biased attribute values.

Klíčová slova:

Calculus – Decision making – Data processing – Environmental impacts – Distance measurement – Pattern recognition receptors – Real numbers


Zdroje

1. Bustince H, Barrenechea E, Pagola M, Fernandez J, Xu Z, Bedregal B. A historical account of types of fuzzy sets and their relationships. IEEE Transactions on Fuzzy Systems 2016; 24(1): 179–194.

2. Zadeh LA. Fuzzy sets. Information and control 1965; 8(3): 338–353.

3. Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets and Systems 1986; 20:87–96

4. De SK, Biswas R, Roy AR. Some operations on intuitionistic fuzzy sets. Fuzzy Sets and Systems 2000; 114(3): 477–484.

5. Liu P, Li D. Some Muirhead mean operators for intuitionistic fuzzy numbers and their applications to group decision making. PloS one 2017; 12(1): e0168767. doi: 10.1371/journal.pone.0168767 28103244

6. Liao H, Xu Z. Priorities of intuitionistic fuzzy preference relation based on multiplicative consistency. IEEE Transactions on Fuzzy Systems 2014; 22(6): 1669–1681.

7. Lei Q, Xu Z. Chain and substitution rules of intuitionistic fuzzy calculus. IEEE Transactions on Fuzzy Systems 2016; 24(3): 519–529.

8. Liu P, Chen SM. Group decision making based on Heronian aggregation operators of intuitionistic fuzzy numbers. IEEE Transactions on Cybernetics 2017; 47(9): 2514–2530. doi: 10.1109/TCYB.2016.2634599 28029636

9. Yager RR. Pythagorean membership grades in multicriteria decision-making. IEEE Transactions on Fuzzy Systems 2014; 22(4): 958–965

10. Yager RR, Abbasov AM. Pythagorean membership grades, complex numbers, and decision-making. International Journal of Intelligent Systems 2013; 28(5): 436–452.

11. Peng X, Yang Y. Some results for Pythagorean fuzzy sets. International Journal of Intelligent Systems 2015; 30: 1133–1160.

12. Dick S, Yager RR, Yazdanbakhsh O. On Pythagorean and complex fuzzy set operations. IEEE Transactions on Fuzzy Systems 2016; 24(5): 1009–1021.

13. Liang D, Xu Z, Liu D, Wu Y. Method for three-way decisions using ideal TOPSIS solutions at Pythagorean fuzzy information. Information Sciences 2018; 435: 282–295.

14. Yager RR. Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems 2017; 25(5): 1222–1230.

15. Peng X, Dai J, Garg H. Exponential operation and aggregation operator for q-rung orthopair fuzzy set and their decision-making method with a new score function. International Journal of Intelligent Systems 2018; 33(11): 2255–2282.

16. Peng X, Dai J. Research on the assessment of classroom teaching quality with q-rung orthopair fuzzy information based on multiparametric similarity measure and combinative distance-based assessment. International Journal of Intelligent Systems 2019; 34(7): 1588–1630.

17. Du W. Minkowski-type distance measures for generalized orthopair fuzzy sets. International Journal of Intelligent Systems 2018; 33: 802–817.

18. Du W. Correlation and correlation coefficient of generalized orthopair fuzzy sets. International Journal of Intelligent Systems 2019; 34(4): 564–583.

19. Wang H, Ju Y, Liu P. Multi‐attribute group decision‐making methods based on q‐rung orthopair fuzzy linguistic sets. International Journal of Intelligent Systems 2019; 34(6): 1129–1157.

20. Liu P, Wang P. Multiple-attribute decision-making based on Archimedean Bonferroni Operators of q-rung orthopair fuzzy numbers. IEEE Transactions on Fuzzy Systems 2018; DOI: doi: 10.1109/TFUZZ.2018.2826452

21. Gao H, Ju Y, Zhang W, Ju D. Multi-Attribute Decision-Making Method Based on Interval-valued q-Rung Orthopair Fuzzy Archimedean Muirhead Mean Operators. IEEE Access 2019; 7: 74300–74315.

22. Liu P. Some Hamacher aggregation operators based on the interval-valued intuitionistic fuzzy numbers and their application to group decision making. IEEE Transactions on Fuzzy systems 2013; 22(1): 83–97.

23. Zhang X, Liu P, Wang Y. Multiple attribute group decision making methods based on intuitionistic fuzzy frank power aggregation operators. Journal of Intelligent & Fuzzy Systems 2015; 29(5): 2235–2246.

24. Yager RR. The power average operator. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 2001; 31(6): 724–731.

25. Liu P, Wang P. Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. International Journal of Intelligent Systems2018; 33(2): 259–280.

26. Liu P, Liu J. Some q-rung orthopai fuzzy Bonferroni mean operators and their application to multi-attribute group decision making. International Journal of Intelligent Systems 2018; 33(2): 315–347.

27. Liu P, Liu W. Multiple‐attribute group decision‐making based on power Bonferroni operators of linguistic q‐rung orthopair fuzzy numbers. International Journal of Intelligent Systems 2019; 34(4): 652–689.

28. Yang W, Pang Y. New q‐rung orthopair fuzzy partitioned Bonferroni mean operators and their application in multiple attribute decision making. International Journal of Intelligent Systems 2019; 34(3): 439–476.

29. Liu Z, Liu P, Liang X. Multiple attribute decision-making method for dealing with heterogeneous relationship among attributes and unknown attribute weight information under q-rung orthopair fuzzy environment. International Journal of Intelligent Systems 2018; 33(9): 1900–1928

30. Wei G, Wei C, Wang J, Gao H, Wei Y. Some q‐rung orthopair fuzzy maclaurin symmetric mean operators and their applications to potential evaluation of emerging technology commercialization. International Journal of Intelligent Systems 2018, 34(1): 50–81.

31. Bai K, Zhu X, Wang J, Zhang R. Some partitioned Maclaurin symmetric mean based on q-rung orthopair fuzzy information for dealing with multi-attribute group decision making. Symmetry 2018; 10: 383.

32. Liu P, Chen SM, Wang P. Multiple-attribute group decision-making based on q-rung orthopair fuzzy power maclaurin symmetric mean operators. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2018; DOI: doi: 10.1109/TSMC.2018.2852948

33. Xing Y, Zhang R, Wang J, Bai K, Xue J. A new multi-criteria group decision-making approach based on q-rung orthopair fuzzy interaction Hamy mean operators. Neural Computing and Applications 2019; DOI: doi: 10.1007/ s00521-019-04269-8

34. Wang J, Zhang R, Zhu X, Zhou Z, Shang X, Li W.Some q-rung orthopair fuzzy Muirhead means with their application to multi-attribute group decision making. Journal of Intelligent & Fuzzy Systems 2019; 36(2): 1599–1614.

35. Liu P, Liu W. Multiple‐attribute group decision‐making method of linguistic q‐rung orthopair fuzzy power Muirhead mean operators based on entropy weight. International Journal of Intelligent Systems 2019; 34(8): 1755–1794.

36. Xing Y, Zhang R, Zhou Z, Wang J.Some q-rung orthopair fuzzy point weighted aggregation operators for multi-attribute decision making. Soft Computing 2019; https://doi.org/10.1007/s00500-018-03712-7.

37. Wei G, Gao H, Wei Y. Some q-rung orthopair fuzzy Heronian mean operators in multiple attribute decision making. International Journal of Intelligent Systems 2018; 33(7): 1426–1458.

38. Liu Z, Wang S, Liu P. Multiple attribute group decision making based on q-rung orthopair fuzzy Heronian mean operators. International Journal of Intelligent Systems 2018; 33(12): 2341–2363.

39. Yu D, Wu Y. Interval-valued intuitionistic fuzzy Heronian mean operators and their application in multi-criteria decision making. African Journal of Business Management 2012; 6(11): 4158–4168.

40. Liu P, Liu J, Merigo JM. Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision making. Applied Soft Computing 2018; 62: 395–422.

41. Dombi J. A general class of fuzzy operators, the DeMorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Sets Systems 1982; 8(2):149–163.

42. Liu P, Liu J, Chen SM. Some intuitionistic fuzzy Dombi Bonferroni mean operators and their application to multi-attribute group decision making. Journal of the Operational Research Society 2018; 69(1): 1–24.

43. He X. Typhoon disaster assessment based on Dombi hesitant fuzzy information aggregation operators. Natural Hazards 2018; 90(3): 1153–1175.

44. Chen J, Ye J. Some single-valued neutrosophic Dombi weighted aggregation operators for multiple attribute decision-making. Symmetry 2017; 9: 82.

45. Beliakov G, Pradera A, Calvo T. Aggregation Functions: A Guide for Practitioners. Berlin, Germany: Springer; 2007

46. Klement EP, Mesiar R. Eds., Logical, Algebraic, Analytic, and Probabilistic Aspects of Triangular Norms. New York, NY, USA: Elsevier 2005.


Článok vyšiel v časopise

PLOS One


2019 Číslo 10
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#