PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wynik贸w
Tytu艂 artyku艂u

Linguistic 饾憺-rung orthopair fuzzy prioritized aggregation operators based on Hamacher 饾憽-norm and 饾憽-conorm and their applications to multicriteria group decision making

Tre艣膰 / Zawarto艣膰
Identyfikatory
Warianty tytu艂u
J臋zyki publikacji
EN
Abstrakty
EN
The linguistic q-rung orthopair fuzzy (Lq-ROF) set is an important implement in the research area in modelling vague decision information by incorporating the advantages of q-rung orthopair fuzzy sets and linguistic variables. This paper aims to investigate the multicriteria decision group decision making (MCGDM) with Lq-ROF information. To do this, utilizing Hamacher t-norm and t-conorm, some Lq-ROF prioritized aggregation operators viz., Lq-ROF Hamacher prioritized weighted averaging, and Lq-ROF Hamacher prioritized weighted geometric operators are developed in this paper. The defined operators can effectively deal with different priority levels of attributes involved in the decision making processes. In addition, Hamacher parameters incorporated with the proposed operators make the information fusion process more flexible. Some prominent characteristics of the developed operators are also well-proven. Then based on the proposed aggregation operators, an MCGDM model with Lq-ROF context is framed. A numerical example is illustrated in accordance with the developed model to verify its rationality and applicability. The impacts of Hamacher and rung parameters on the achieved decision results are also analyzed in detail. Afterwards, a comparative study with other representative methods is presented in order to reflect the validity and superiority of the proposed approach.
Rocznik
Strony
451--484
Opis fizyczny
Bibliogr. 54 poz., rys., tab., wzory
Tw贸rcy
autor
  • Department of Mathematics, University of Kalyani, Kalyani - 741235, India
autor
  • Department of Mathematics, Heramba Chandra College, Kolkata - 700029, India
  • Department of Mathematics, University of Kalyani, Kalyani - 741235, India
Bibliografia
  • [1] L.A. Zadeh: Fuzzy sets. Information and Control, 8(3), (1965), 338-353. DOI: 10.1016/S0019-9958(65)90241-X.
  • [2] K.T. Atanassov: Intuitionistic fuzzy sets, Fuzzy Sets and Systems. 20 (1986), 87-96. DOI: 10.1016/S0165-0114(86)80034-3.
  • [3] K.T. Atanassov and G. Gargov: Interval-valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 31(3), (1989), 343-349. DOI: 10.1016/0165-0114(89)90205-4.
  • [4] R.R. Yager: Pythagorean fuzzy subsets. In: Proceeding of The Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, Canada, (2013), 57-61. DOI: 10.1109/IFSA-NAFIPS.2013.6608375.
  • [5] R.R. Yager: Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), (2014), 958-965. DOI: 10.1109/TFUZZ.2013.2278989.
  • [6] X. Peng and Y. Yang: Fundamental properties of interval-valued Pythagorean fuzzy aggregation operators. International Journal of Intelligent Systems, 31(5), (2016), 444-487. DOI: 10.1002/int.21790.
  • [7] K. Rahman and S. Abdullah: Some induced generalized geometric aggregation operators based on interval-valued Pythagorean fuzzy numbers. Mathematical Sciences, 14(4), (2020), 397-407. DOI: 10.1007/s40096-020-00350-9.
  • [8] T. Senapati and R.R. Yager: Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11 (2020), 663-674. DOI: 10.1007/s12652-019-01377-0.
  • [9] S. Kumar and A. Biswas: A unified TOPSIS approach to MADM problems in interval-valued intuitionistic fuzzy environment. In: N. Verma and A. Ghosh (Eds), Computational Intelligence: Theories, Applications and Future Directions - Volume II, Advances in Intelligent Systems and Computing Springer, Singapore, (2019), 799. DOI: 10.1007/978-981-13-1135-2_33.
  • [10] A. Biswas and S. Kumar: Generalization of extent analysis method for solving multicriteria decision making problems involving intuitionistic fuzzy numbers. OPSEARCH, 56(2019), 1142-1166. DOI: 10.1007/s12597-019-00413-z.
  • [11] A. Biswas and B. Sarkar: Interval-valued Pythagorean fuzzy TODIM approach through point operator-based similarity measures for multicriteria group decision making. Kybernetes, 48(3), (2019), 496-519. DOI: 10.1108/K-12-2017-0490.
  • [12] A. Sarkar, and A. Biswas: Multicriteria decision-making using Archimedean aggregation operators in Pythagorean hesitant fuzzy environment. International Journal of Intelligent Systems, 34(7), (2019), 1361-1386. DOI: 10.1002/int.22099.
  • [13] A. Biswas and N. Deb: Pythagorean fuzzy Schweizer and Sklar power aggregation operators for solving multi-attribute decision-making problems. Granular Computing, 6(1), (2021), 991-1007. DOI: 10.1007/s41066-020-00243-1.
  • [14] A. Sarkar and A. Biswas: Interval-valued dual hesitant fuzzy prioritized aggregation operators based on Archimedean 饾憽-conorm and 饾憽-norm and their applications to multi-criteria decision making. Archives of Control Sciences, 31(1), (2021), 213-247. DOI: 10.24425/acs.2021.136887.
  • [15] G. Wei and M. Lu: Dual hesitant Pythagorean fuzzy Hamacher aggregation operators in multiple attribute decision making. Archives of Control Sciences, 27(3), (2017), 365-395. DOI: 10.1515/acsc-2017-0024.
  • [16] R.R. Yager: Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), (2017), 1222-1230. DOI: 10.1109/TFUZZ.2016.2604005.
  • [17] P. Liu and P. Wang: Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. International Journal of Intelligent Systems, 33(2), (2018), 259-280. DOI: 10.1002/int.21927.
  • [18] P. Liu and P. Wang: Multiple-attribute decision-making based on Archimedean Bonferroni Operators of q-rung orthopair fuzzy numbers. IEEE Transactions on Fuzzy Systems, 27(5), (2018), 834-848. DOI: 10.1109/TFUZZ.2018.2826452.
  • [19] G. Wei, H. Gao and Y. Wei: Some q-rung orthopair fuzzy Heronian mean operators in multiple attribute decision making. International Journal of Intelligent Systems, 33(7), (2018), 1426-1458. DOI: 10.1002/int.21985.
  • [20] P. Wang, J. Wang, G. Wei and C. Wei: Similarity measures of q-rung orthopair fuzzy sets based on cosine function and their applications. Mathematics, 7(4), (2019), 740. DOI: 10.3390/math7040340.
  • [21] S. Zeng, Y. Hu and X. Xie: Q-rung orthopair fuzzy weighted induced logarithmic distance measures and their application in multiple attribute decision making. Engineering Applications of Artificial Intelligence, 100 (2021), 104167. DOI: 10.1016/j.engappai.2021.104167.
  • [22] M. Riaz, W. Sa艂abun, H.M. Athar Farid, N. Ali and J. W膮tr贸bski: A robust q-rung orthopair fuzzy information aggregation using Einstein operations with application to sustainable energy planning decision management. Energies, 13(9), (2020), 2155. DOI: 10.3390/en13092155.
  • [23] A. Pinar and F.E. Boran: A q-rung orthopair fuzzy multicriteria group decision making method for supplier selection based on a novel distance measure. International Journal of Machine Learning and Cybernetics, 11 (2020), 1749-1780. DOI: 10.1007/s13042-020-01070-1.
  • [24] H. Garg and S.M. Chen: Multiattribute group decision making based on neutrality aggregation operators of q-rung orthopair fuzzy sets. Information Sciences, 517 (2020), 427-447. DOI: 10.1016/j.ins.2019.11.035.
  • [25] M. Akram, G. Shahzadi and X. Peng: Extension of Einstein geometric operators to multi-attribute decision making under q-rung orthopair fuzzy information. Granular Computing, 6 (2021), 779-795. DOI: 10.1007/s41066-020-00233-3.
  • [26] M.J. Khan, P. Kumam and M. Shutaywi: Knowledge measure for the q-rung orthopair fuzzy sets. International Journal of Intelligent Systems, 36(8), (2021), 628-655. DOI: 10.1002/int.22313.
  • [27] N. Alkan and C. Kahraman: Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method. Applied Soft Computing, 110 (2021), 107653. DOI: 10.1016/j.asoc.2021.107653.
  • [28] B. Zhao, R. Zhang and Y. Xin: Evaluation of medical service quality based on a novel multi-criteria decision-making method with unknown weighted information. Archives of Control Sciences, 31(3), (2021), 365-395. DOI: 10.24425/acs.2021.138696.
  • [29] P. Liu and W. Liu: Multiple-attribute group decision-making based on power Bonferroni operators of linguistic q-rung orthopair fuzzy numbers. International Journal of Intelligent Systems, 34(12), (2019), 652-689. DOI: 10.1002/int.22071.
  • [30] L.A. Zadeh: The concept of a linguistic variable and its application to approximate reasoning - I, Information sciences, 8(4), (1975), 199-249. DOI: 10.1016/0020-0255(75)90046-8.
  • [31] Z. Chen, P. Liu and Z. Pei: An approach to multiple attribute group decision making based on linguistic intuitionistic fuzzy numbers. International Journal of Computational Intelligence Systems, 8(4), (2015), 747-760. DOI: 10.1080/18756891.2015.1061394.
  • [32] H. Garg: Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision-making process. International Journal of Intelligent Systems, 33(6), (2018), 1234-1263. DOI: 10.1002/int.21979.
  • [33] D. Peng, J. Wang, D. Liu and Z. Liu: The similarity measures for linguistic q-rung orthopair fuzzy Mmulti-criteria group decision making using projection method. In IEEE Access, 7 (2019), 176732-176745. DOI: 10.1109/ACCESS.2019.2957916.
  • [34] R. Verma: Generalized similarity measures under linguistic q-rung orthopair fuzzy environment with application to multiple attribute decision-making. Granular Computing, 7 (2021), 253-275. DOI: 10.1007/s41066-021-00264-4.
  • [35] D. Liu and A. Huang: Consensus reaching process for fuzzy behavioral TOPSIS method with probabilistic linguistic q-rung orthopair fuzzy set based on correlation measure. International Journal of Intelligent Systems, 35(11), (2020), 494-528. DOI: 10.1002/int.22215.
  • [36] D. Liu, Y. Liu and L. Wang: The reference ideal TOPSIS method for linguistic q-rung orthopair fuzzy decision making based on linguistic scale function. Journal of Intelligent & Fuzzy Systems, 39(3), (2020), 4111-4131. DOI: 10.3233/JIFS-200244.
  • [37] M. Lin, X. Li and L. Chen: Linguistic q-rung orthopair fuzzy sets and their interactional partitioned Heronian mean aggregation operators. International Journal of Intelligent Systems, 35(2), (2019), 217-249. DOI: 10.1002/int.22136.
  • [38] P. Liu and W. Liu: 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, 34(3), (2019), 1755-1794. DOI: 10.1002/int.22114.
  • [39] M. Akram, S. Naz, S.A. Edalatpanah and R. Mehreen: Group decision-making framework under linguistic q-rung orthopair fuzzy Einstein models. Soft Computing, 25 (2021), 10309-10334. DOI: 10.1007/s00500-021-05771-9.
  • [40] P. Liu, S. Naz, M. Akram and M. Muzammal: Group decision-making analysis based on linguistic q-rung orthopair fuzzy generalized point weighted aggregation operators. International Journal of Machine Learning and Cybernetics, 13 (2022), 883-906. DOI: 10.1007/s13042-021-01425-2.
  • [41] J. Wang, X. Shang, X. Feng and M. Sun: A novel multiple attribute decision making method based on q-rung dual hesitant uncertain linguistic sets and Muirhead mean. Archives of Control Sciences, 30(2), (2020), 233-272. DOI: 10.24425/acs.2020.133499.
  • [42] R.R. Yager: Prioritized aggregation operators, International Journal of Approximate Reasoning, 48(1), (2008), 263-274. DOI: 10.1016/j.ijar.2007. 08.009.
  • [43] H. Hamachar: Uber logische verknunpfungenn unssharfer aussagen undderen zugen horige bewertungsfunktione, Progress in Cybernetics and Systems Research, III; Trappl, R., Klir, G.J., Ricciardi, L., Eds, 3 (1978), 276-288.
  • [44] A. Fahmi, S. Abdullah and F. Amin: Cubic uncertain linguistic powered Einstein aggregation operators and their application to multi-attribute 484 N. DEB, A. SARKAR, A. BISWAS group decision making. Mathematical Sciences, 13 (2019), 129-152. DOI: 10.1007/s40096-019-0285-5.
  • [45] H. Gao: Pythagorean fuzzy Hamacher prioritized aggregation operators in multiple attribute decision making. Journal of Intelligent & Fuzzy Systems, 35(2), (2018), 2229-2245. DOI: 10.3233/JIFS-172262.
  • [46] G. Wei, M. Lu, X. Tang and Y. Wei: Pythagorean hesitant fuzzy hamacher aggregation operators and their application to multiple attribute decision making. International Journal of Intelligent Systems, 33(6), (2018), 1197-1233. DOI: 10.1002/int.21978.
  • [47] A. Jan, A. Khan, W. Khan and M. Afridi: A novel approach to MADM problems using Fermatean fuzzy Hamacher prioritized aggregation operators. Soft Computing, 25 (2021), 13897-13910. DOI: 10.1007/s00500-021-06308-w.
  • [48] F. Herrera, E. Herrera-Viedma and J.L. Verdegay: A model of consensus in group decision making under linguistic assessments. Fuzzy Sets and Systems, 78(1), (1996), 73-87. DOI: 10.1016/0165-0114(95)00107-7.
  • [49] Z.S. Xu: A method based on linguistic aggregation operators for group decision making under linguistic preference relations. Information Sciences, 166(1-4), (2004), 19-30. DOI: 10.1016/j.ins.2003.10.006.
  • [50] R. Arora and H. Garg: Group decision-making method based on prioritized linguistic intuitionistic fuzzy aggregation operators and its fundamental properties. Computational and Applied Mathematics, 38 (2019), 1-32. DOI: 10.1007/s40314-019-0764-1.
  • [51] H. Garg, and K. Kumar: Group decision making approach based on possibility degree measures and the linguistic intuitionistic fuzzy aggregation operators using Einstein norm operations. Journal of Multiple-Valued Logic & Soft Computing, 31 (2018), 175-209. DOI: 10.3934/jimo.2018162.
  • [52] J. Tang and F. Meng: Linguistic intuitionistic fuzzy Hamacher aggregation operators and their application to group decision making. Granular Computing, 4 (2019), 109-124. DOI: 10.1007/s41066-018-0089-2.
  • [53] Y. Rong, Z. Pei and Y. Liu: Linguistic Pythagorean Einstein operators and their application to decision making. Information, 11(1), (2020), 46. DOI: 10.3390/info11010046.
  • [54] Y. Liu, Y. Qin, L. Xu, H.B. Liu and J. Liu: Multiattribute group decisionmaking approach with linguistic Pythagorean fuzzy information. IEEE Access, 7 (2019), 143412-143430. DOI: 10.1109/ACCESS.2019.2945005.
Uwagi
Opracowanie rekordu ze 艣rodk贸w MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Spo艂eczna odpowiedzialno艣膰 nauki" - modu艂: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-be9b7736-b37a-43c3-8035-e3ec2acc7bfc
JavaScript jest wy艂膮czony w Twojej przegl膮darce internetowej. W艂膮cz go, a nast臋pnie od艣wie偶 stron臋, aby m贸c w pe艂ni z niej korzysta膰.