Probabilistic linguistic terms set (PLTS), a new tool for expressing uncertain decision information, is composed of all possible linguistic terms (LTs) and their related probabilities. It also increases the corresponding probability of LTs in hesitant fuzzy linguistic term set (HFLTS). On the other hand, aggregation operator is an important information fusion tool, the Maclaurin symmetric mean (MSM) operator can provide more flexibility and robustness in information fusion, and make it more suitable for solving MADM problems with independent attributes. This current study adopts the merits of PLTS and MSM operator, and then a novel probabilistic linguistic decision making approach are targeted. Firstly, the operations of two PLTSs are redefined based upon Archimedean t-norm (ATN) and Archimedean t-conorm (ATC); Secondly, the probabilistic linguistic generalized MSM operator (PLGMSM) is proposed based on ATN and ATC, some properties of PLGMSM are investigated, then some special PLGMSM operators have been studied in detail when the parameters take different values and the generator of ATN takes different functions. Thirdly, the weighted probabilistic linguistic generalized MSM operator (WPLGMSM) is studied along with some properties of PLGMSM, some special WPLGMSM operators have been also investigated in detail when the parameters take different values and the generator of ATN takes different functions. Finally, on the basis of our proposed aggregation operators, the aggregated-based decision making approach is designed and an example is supplied to manifest the effectiveness of the proposed approach. Furthermore, some comparison analyses with extant decision approaches are carried out to illustrate the validity and feasibility of the proposed approach.
Citation: Ya Qin, Siti Rahayu Mohd. Hashim, Jumat Sulaiman. Probabilistic linguistic multi-attribute decision making approach based upon novel GMSM operators[J]. AIMS Mathematics, 2023, 8(5): 11727-11751. doi: 10.3934/math.2023594
Probabilistic linguistic terms set (PLTS), a new tool for expressing uncertain decision information, is composed of all possible linguistic terms (LTs) and their related probabilities. It also increases the corresponding probability of LTs in hesitant fuzzy linguistic term set (HFLTS). On the other hand, aggregation operator is an important information fusion tool, the Maclaurin symmetric mean (MSM) operator can provide more flexibility and robustness in information fusion, and make it more suitable for solving MADM problems with independent attributes. This current study adopts the merits of PLTS and MSM operator, and then a novel probabilistic linguistic decision making approach are targeted. Firstly, the operations of two PLTSs are redefined based upon Archimedean t-norm (ATN) and Archimedean t-conorm (ATC); Secondly, the probabilistic linguistic generalized MSM operator (PLGMSM) is proposed based on ATN and ATC, some properties of PLGMSM are investigated, then some special PLGMSM operators have been studied in detail when the parameters take different values and the generator of ATN takes different functions. Thirdly, the weighted probabilistic linguistic generalized MSM operator (WPLGMSM) is studied along with some properties of PLGMSM, some special WPLGMSM operators have been also investigated in detail when the parameters take different values and the generator of ATN takes different functions. Finally, on the basis of our proposed aggregation operators, the aggregated-based decision making approach is designed and an example is supplied to manifest the effectiveness of the proposed approach. Furthermore, some comparison analyses with extant decision approaches are carried out to illustrate the validity and feasibility of the proposed approach.
[1] | L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X |
[2] | K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets Syst., 20 (1986), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3 |
[3] | I. B. Turksen, Interval valued fuzzy sets based on normal forms, Fuzzy Sets Syst., 20 (1986), 191–210. https://doi.org/10.1016/0165-0114(86)90077-1 doi: 10.1016/0165-0114(86)90077-1 |
[4] | V. Torra, Hesitant fuzzy sets, Int. J. Intell. Syst., 25 (2010), 529–539. https://doi.org/10.1002/int.20418 |
[5] | B. Zhu, Z. S. Xu, M. M. Xia, Dual hesitant fuzzy sets, J. Appl. Math., 2012 (2012), 879629. https://doi.org/10.1155/2012/879629 |
[6] | L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Inf. Sci., 8 (1975), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5 doi: 10.1016/0020-0255(75)90036-5 |
[7] | M. Yazdi, Linguistic methods under fuzzy information in system safety and reliability analysis, In: Studies in Fuzziness and Soft Computing, Springer Cham, 414 (2022). https://doi.org/10.1007/978-3-030-93352-4 |
[8] | H. Li, M. Yazdi, Advanced decision-making methods and applications in system safety and reliability problems, In: Studies in Systems, Decision and Control, Springer Cham, 211 (2022). https://doi.org/10.1007/978-3-031-07430-1 |
[9] | Z. S. Xu, Induced uncertain linguistic OWA operators applied to group decision making, Inform. Fusion, 7 (2006), 231–238. https://doi.org/10.1016/j.inffus.2004.06.005 doi: 10.1016/j.inffus.2004.06.005 |
[10] | R. M. Rodriguez, L. Martinez, F. Herrera, Hesitant fuzzy linguistic term sets for decision making, IEEE T. Fuzzy Syst., 20 (2012), 109–119. https://doi.org/10.1109/TFUZZ.2011.2170076 doi: 10.1109/TFUZZ.2011.2170076 |
[11] | H. Wang, Extended hesitant fuzzy linguistic term sets and their aggregation in group decision making, Int. J. Comput. Intell. Syst., 8 (2015), 14–33. https://doi.org/10.1080/18756891.2014.964010 doi: 10.1080/18756891.2014.964010 |
[12] | H. Wang, Z. S. Xu, X. J. Zeng, Linguistic terms with weakened hedges: A model for qualitative decision making under uncertainty, Inform. Sci., 433–434 (2018), 37–54. https://doi.org/10.1016/j.ins.2017.12.036 doi: 10.1016/j.ins.2017.12.036 |
[13] | S. Karimi, K. N. Papamichail, C. P. Holland, The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour, Decis. Support Syst., 77 (2015), 137–147. https://doi.org/10.1016/j.dss.2015.06.004 doi: 10.1016/j.dss.2015.06.004 |
[14] | S. Heblich, A. Lameli, G. Riener, The effect of perceived regional accents on individual economic behavior: A lab experiment on linguistic performance, cognitive ratings and economic decisions, Plos One, 10 (2015), e0113475. https://doi.org/10.1371/journal.pone.0113475 doi: 10.1371/journal.pone.0113475 |
[15] | V. Torra, Hesitant fuzzy sets, Int. J. Intell. Syst., 25 (2010), 529–539. https://doi.org/10.1002/int.20418 |
[16] | Q. Pang, H. Wang, Z. S. Xu, Probabilistic linguistic term sets in multi-attribute group decision making, Inform. Sci., 369 (2016), 128–143. https://doi.org/10.1016/j.ins.2016.06.021 doi: 10.1016/j.ins.2016.06.021 |
[17] | P. D. Liu, F. Teng, Multiple attribute group decision making methods based on some normal neutrosophic number Heronian Mean operators, J. Intell. Fuzzy Syst., 32 (2017), 2375–2391. https://doi.org/10.3233/JIFS-16345 doi: 10.3233/JIFS-16345 |
[18] | M. Qiyas, S. Abdullah, Y. Liu, M. Naeem, Multi-criteria decision support systems based on linguistic intuitionistic cubic fuzzy aggregation operators, J. Amb. Intel. Hum. Comp., 12 (2022), 8285–8303. |
[19] | M. Qiyas, T. Madrar, S. Khan, S. Abdullah, T. Botmart, Decision support system based on fuzzy credibility Dombi aggregation operators and modified TOPSIS method, AIMS Mathematics, 10 (2022), 19057–19082. https://doi.org/10.3934/math.20221047 doi: 10.3934/math.20221047 |
[20] | M. Qiyas, N. Khan, M. Naeem, S. Abdullah, Intuitionistic fuzzy credibility Dombi aggregation operators and their application of railway train selection in Pakistan, AIMS Mathematics, 8 (2023), 6520–6542. https://doi.org/10.3934/math.2023329 doi: 10.3934/math.2023329 |
[21] | C. Maclaurin, A second letter to Martin Folkes, Esq.; concerning the roots of equations, with the demonstration of other rules in algebra, Phil. Transactions, 36 (1729), 59–96. https://doi.org/10.1098/rstl.1729.0011 doi: 10.1098/rstl.1729.0011 |
[22] | D. J. Yu, Hesitant fuzzy multi-criteria decision making methods based on Heronian mean, Technol. Econ. Dev. Eco., 23 (2017), 296–315. https://doi.org/10.3846/20294913.2015.1072755 doi: 10.3846/20294913.2015.1072755 |
[23] | J. D. Qin, X. W. Liu, Approaches to uncertain linguistic multiple attribute decision making based on dual Maclaurin symmetric mean, J. Intell. Fuzzy Syst., 29 (2015), 171–186. https://doi.org/10.3233/IFS-151584 doi: 10.3233/IFS-151584 |
[24] | J. D. Qin, X. W. Liu, W. Pedrycz, Hesitant fuzzy Maclaurin symmetric mean operators and its application to multiple-attribute decision making, Int. J. Fuzzy Syst., 17 (2015), 509–520. |
[25] | R. F. Muirhead, Some methods applicable to identities and inequalities of symmetric algebraic functions of n letters, Proc. Edinb. Math. Soc., 21 (1902), 144–162. https://doi.org/10.1017/S001309150003460X doi: 10.1017/S001309150003460X |
[26] | P. D. Liu, D. F. Li, Some muirhead mean operators for intuitionistic fuzzy numbers and their applications to group decision making, PloS One, 12 (2017), e0168767. https://doi.org/10.1371/journal.pone.0168767 doi: 10.1371/journal.pone.0168767 |
[27] | P. D. Liu, F. Teng, Some muirhead mean operators for probabilistic linguistic term sets and their applications to multiple attribute decision-making, Appl. Soft Comput. J., 68 (2018), 396–431 https://doi.org/10.1016/j.asoc.2018.03.027 doi: 10.1016/j.asoc.2018.03.027 |
[28] | G. Beliakov, S. James, J. Mordelová, T. Rückschlossová, R. R. Yager, Generalized Bonferroni mean operators in multi-criteria aggregation, Fuzzy Sets Syst., 161 (2010), 2227-2242. https://doi.org/10.1016/j.fss.2010.04.004 doi: 10.1016/j.fss.2010.04.004 |
[29] | Y. D. He, Z. He, H. Y. Chen, Intuitionistic fuzzy interaction Bonferroni means and its application to multiple attribute decision making, IEEE Trans. Cybernetics, 45 (2015), 116–128. https://doi.org/10.1109/TCYB.2014.2320910 doi: 10.1109/TCYB.2014.2320910 |
[30] | Z. S. Xu, R. R. Yager, Intuitionistic fuzzy Bonferroni means, IEEE Trans. Syst. Man Cy. B, 41 (2011), 568–578. https://doi.org/10.1109/TSMCB.2010.2072918 doi: 10.1109/TSMCB.2010.2072918 |
[31] | P. D. Liu, S. M. Chen, Group decision making based on Heronian aggregation operators of intuitionistic fuzzy numbers, IEEE Trans. Cybernetics, 47 (2016), 2514–2530. https://doi.org/10.1109/TCYB.2016. 2634599 doi: 10.1109/TCYB.2016.2634599 |
[32] | J. D. Qin, X. W. Liu, An approach to intuitionistic fuzzy multiple attribute decision making based on Maclaurin symmetric mean operators, J. Intell. Fuzzy Syst., 27 (2014), 2177–2190. |
[33] | P. D. Liu, X. H. Zhang, Some Maclaurin symmetric mean operators for single-valued trapezoidal neutrosophic numbers and their applications to group decision making, Int. J. Fuzzy Syst., 20 (2018), 45–61 https://doi.org/10.1007/s40815-017-0335-9 doi: 10.1007/s40815-017-0335-9 |
[34] | Y. B. Ju, X. Y. Liu, D. W. Ju, Some new intuitionistic linguistic aggregation operators based on Maclaurin symmetric mean and their applications to multiple attribute group decision making, Soft Comput., 20 (2016), 4521–4548. |
[35] | D. Detemple, J. Robertson, On generalized symmetric means of two variables, Univ. Beograd. Publ. Elektrotehn. Fak. Ser. Mat. Fiz., 634 (1979), 236–238 https://doi:10.1002/anie.200704684 doi: 10.1002/anie.200704684 |
[36] | F. Herrera, E. Herrera-Viedma, Linguistic decision analysis: steps for solving decision problems under linguistic information, Fuzzy Sets Syst., 115 (2000), 67–82. https://doi.org/10.1016/S0165-0114(99)00024-X doi: 10.1016/S0165-0114(99)00024-X |
[37] | X. Gou, Z. Xu, H. Liao, Multiple criteria decision making based on Bonferroni means with hesitant fuzzy linguistic information, Soft Comput., 21 (2017), 6515–6529. |
[38] | E. P. Klement, R. Mesiar, E. Pap, Triangular norms, In: Trends in Logic, Springer Dordrecht, 2000. https://doi.org/10.1007/978-94-015-9540-7 |
[39] | K. X. Zhao, X. J. Huang, Probabilistic linguistic hesitant fuzzy set and its application in multiple attribute decision-making, J. Nanchang Univ. (Natural Sci.), 41 (2017), 511–518. https://doi.org/10.3969/j.issn.1006-0464.2017.06.001 doi: 10.3969/j.issn.1006-0464.2017.06.001 |
[40] | B. Fang, B. Han, D. Y. Xie, Probabilistic linguistic multi-attribute decision-making method based on possibility degree matrix, Control Decis., 37 (2022), 2149–2156. https://doi.org/10.13195/j.kzyjc.2021.0350 doi: 10.13195/j.kzyjc.2021.0350 |