Abstract
Hesitant fuzzy linguistic term set (HFLTS) is a powerful tool for solving the situations in which a decision maker hesitates among several consecutive linguistic terms in providing his or her preference to an alternative. To reflect the different importance degrees or weights of all possible linguistic terms in a HFLTS, an extension of HFLTS called probabilistic linguistic term set (PLTS) is proposed through adding probabilities. Note that for a PLTS, the importance information of all possible linguistic terms is described by crisp numbers. However, in practical applications, especially under uncertain environment, it may be difficult for decision makers to provide the importance information by crisp numbers. To accurately preserve the complete evaluation information provided by decision makers, motivated by the idea of 2-dimension linguistic variables, this paper proposes the concept of hesitant fuzzy 2-dimension linguistic term set, which includes not only possible linguistic terms expressing the evaluation value to an object, but also the importance degree of each linguistic term denoted by a linguistic term. Firstly, the operations and comparison laws between hesitant fuzzy 2-dimension linguistic elements (HF2DLEs) are defined. Then, some generalized aggregation operators are proposed for aggregating HF2DLEs, such as generalized hesitant fuzzy 2-dimension linguistic weighted average (G-HF2DLWA) operator, generalized hesitant fuzzy 2-dimension linguistic ordered weighted average (G-HF2DLOWA) operator and generalized hesitant fuzzy 2-dimension linguistic hybrid weighted average (G-HF2DLHWA) operator. Furthermore, some desirable properties and special cases of these operators are discussed. Based on the G-HF2DLOWA and G-HF2DLWA operators, an approach to multiple attribute group decision making is developed under hesitant fuzzy 2-dimension linguistic environment. Finally, a numerical example is given to verify the practicality and effectiveness of the proposed method.

Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–356 (1965)
Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)
Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning-Part I. Inf. Sci. 8(3), 199–249 (1975)
Rodríguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic terms sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–119 (2012)
Gou, X.J., Xu, Z.S.: Novel basic operational laws for linguistic terms, hesitant fuzzy linguistic term sets and probabilistic linguistic term sets. Inf. Sci. 372, 407–427 (2016)
Lee, L.W., Chen, S.M.: Fuzzy decision making based on likelihood-based comparison relations of hesitant fuzzy linguistic term sets and hesitant fuzzy linguistic operators. Inf. Sci. 294, 513–529 (2015)
Wang, J.Q., Wang, J., Chen, Q.H., Zhang, H.Y., Chen, X.H.: An outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets. Inf. Sci. 280, 338–351 (2014)
Wei, C.P., Zhao, N., Tang, X.J.: Operators and comparisons of hesitant fuzzy linguistic term sets. IEEE Trans. Fuzzy Syst. 22(3), 575–586 (2014)
Gou, X.J., Xu, Z.S., Liao, H.C.: Hesitant fuzzy linguistic entropy and cross-entropy measures and alternative queuing method for multiple criteria decision making. Inf. Sci. 388–389, 225–246 (2017)
Liao, H.C., Xu, Z.S.: Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for HFLTSs and their application in qualitative decision making. Expert Syst. Appl. 42, 5328–5336 (2015)
Liao, H.C., Xu, Z.S., Zeng, X.J.: Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Inf. Sci. 271, 125–142 (2014)
Liao, H.C., Xu, Z.S., Zeng, X.J., Merigó, J.M.: Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowl. Based Syst. 76, 127–138 (2015)
Wang, J.Q., Wu, J.T., Wang, J., Zhang, H.Y., Chen, X.H.: Multi-criteria decision-making methods based on the Hausdorff distance of hesitant fuzzy linguistic numbers. Soft. Comput. 20(4), 1621–1633 (2016)
Gou, X.J., Xu, Z.S., Liao, H.C.: Multi-criteria decision making based on Bonferroni means with hesitant fuzzy linguistic information. Soft. Comput. (2016). doi:10.1007/s00500-016-2211-1
Xu, Y.J., Xu, A.W., Merigó, J.M., Wang, H.M.: Hesitant fuzzy linguistic ordered weighted distance operators for group decision making. J Appl. Math. Comput. 49(1), 285–308 (2015)
Yu, S.M., Zhang, H.Y., Wang, J.Q.: Hesitant fuzzy linguistic maslaurin symmetric mean operators and their applications to multi-criteria decision-making problem. Int. J. Intell. Syst. (2017). doi:10.1002/int.21907
Farhadinia, B.: Multiple criteria decision-making methods with completely unknown weights in hesitant fuzzy linguistic term setting. Knowl. Based Syst. 93, 135–141 (2016)
Liao, H.C., Xu, Z.S., Zeng, X.J.: Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making. IEEE Trans. Fuzzy Syst. 23, 1343–1355 (2015)
Sellak, H., Ouhbi, B., Frikh, B.: A knowledge-based outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets. Appl. Soft Comput. (2017). doi:10.1016/j.asoc.2017.06.031
Sun, R.X., Hu, J.H., Zhou, J.D., Chen, X.H.: A hesitant fuzzy linguistic projection-based MABAC method for patients’ prioritization. Int. J. Fuzzy Syst. (2017). doi:10.1007/s40815-017-0345-7
Wei, C.P., Ren, Z., Rodríguez, R.M.: A hesitant fuzzy linguistic TODIM method based on score function. Int. J. Comput. Intell. Syst. 22, 575–585 (2015)
Liu, H.B., Cai, J.F., Jiang, L.: On improving the additive consistency of the fuzzy preference relations based on comparative linguistic expressions. Int. J. Intell. Syst. 29, 544–559 (2014)
Zhang, Z.M., Wu, C.: On the use of multiplicative consistency in hesitant fuzzy linguistic preference relations. Knowl. Based Syst. 72, 13–27 (2014)
Zhu, B., Xu, Z.S.: Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Trans. Fuzzy Syst. 22, 35–45 (2014)
Zhang, Z.M., Wu, C.: Hesitant fuzzy linguistic aggregation operators and their applications to multiple attribute group decision making. J. Intell. Fuzzy Syst. 26(5), 2185–2202 (2014)
Wang, J., Wang, J.Q., Zhang, H.Y., Chen, X.H.: Multi-criteria group decision making approach based on 2-tuple linguistic aggregation operators with multi-hesitant fuzzy linguistic information. Int. J. Fuzzy Syst. 18(1), 81–97 (2016)
Gou, X.J., Liao, H.C., Xu, Z.S., Herrera, F.: Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: a case of study to evaluate the implementation status of haze controlling measures. Inf. Fusion 38, 22–34 (2017)
Pang, Q., Wang, H., Xu, Z.S.: Probabilistic linguistic term sets in multi-attribute group decision making. Inf. Sci. 369, 128–143 (2016)
Zhang, Y.X., Xu, Z.S., Wang, H., Liao, H.C.: Consistency-based risk assessment with probabilistic linguistic preference relation. Appl. Soft Comput. 49, 817–833 (2016)
Zhu, W.D., Zhou, G.Z., Yang, S.L.: An approach to group decision making based on 2-dimension linguistic assessment. Syst. Eng. 27(2), 113–118 (2009)
Yager, R.R.: Generalized OWA aggregation operators. Fuzzy Optim. Decis. Mak. 3, 93–107 (2004)
Zhao, H., Xu, Z.S., Ni, M.F., Liu, S.S.: Generalized aggregation operators for intuitionistic fuzzy sets. Int. J. Intell. Syst. 25(1), 1–30 (2010)
Zhou, L.G., Chen, H.Y., Merigó, J.M., Gil-Lafuente, A.M.: Uncertain generalized aggregation operators. Expert Syst. Appl. 39, 1105–1117 (2012)
Li, D.F.: Multiattribute decision making method based on generalized OWA operators with intuitionistic fuzzy sets. Expert Syst. Appl. 37, 8673–8678 (2010)
Wei, G.W.: Some generalized aggregating operators with linguistic information and their application to multiple attribute group decision making. Comput. Ind. Eng. 61, 32–38 (2011)
Xia, M.M., Xu, Z.S.: Hesitant fuzzy information aggregation in decision making. Int. J. Approx. Reason. 52(3), 395–407 (2011)
Herrera, F., Herrera-Viedma, E.: Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Set. Syst. 115(1), 67–82 (2000)
Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A model of consensus in group decision making under linguistic assessment. Fuzzy Set. Syst. 78(1), 73–87 (1996)
Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity of processing information. Psychol. Rev. 63(2), 81–97 (1956)
Xu, Z.S.: Deviation measures of linguistic preference relations in group decision making. Omega 33, 249–254 (2005)
Zhang, J.L., Qi, X.W.: Research on multiple attribute decision making under hesitant fuzzy linguistic environment with application to production strategy decision making. Adv. Mater. Res. 753–755, 2829–2836 (2013)
Liu, P.D., Qi, X.F.: Some generalized dependent aggregation operators with 2-dimension linguistic information and their application to group decision making. J. Intell. Fuzzy Syst. 27, 1761–1773 (2014)
Herrera, F., Herrera-Viedma, E., Chiclana, F.: Multiperson decision-making based on multiplicative preference relations. Eur. J. Oper. Res. 129(2), 372–385 (2001)
Wang, Y., Xu, Z.S.: A new method of giving OWA weights. Math. Prac. Theory 3(3), 51–61 (2008)
Xu, Z.S., Xia, M.M.: Distance and similarity measures for hesitant fuzzy sets. Inf. Sci. 181, 2128–2138 (2011)
Acknowledgements
The work is supported by “the Fundamental Research Funds for the Central Universities” in UIBE (15QD08), National Nature Science Foundation of China (71601066) and the Humanities and Social Science Foundation of Ministry of Education in China (16YJC630093).
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
1.1 The Proof of Theorem 1
Proof (1) For n = 1, Eq. (17) is hold obviously.
(2) For n = 2, since
then
Similarly,
Then,
Based on Eq. (13), we obtain
Therefore, when n = 2, Eq. (17) is hold.
(3) If Eq. (17) holds for n = k, that is
i.e.,
Then, when n = k+1, by Eqs. (10), (12) and (13), we have
i.e., Equation (17) holds for n = k+1. Thus, we can obtain that Eq. (17) holds for all n, which completes the proof of Theorem 1.
1.2 The Proof of Case (2) of the G-HF2DLWA Operator
Proof \(\mathop { \lim }\limits_{\lambda \to 0} {\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }} ,\hat{h}_{{S_{2} }} , \ldots ,\hat{h}_{{S_{n} }} ) = \bigcup\nolimits_{{(\dot{s}_{{a_{i} }} ,\ddot{s}_{{b_{i} }} ) \in \hat{h}_{{S_{i} }} }} {\left\{ {\left( {f^{ - 1} \left( {\left( {1 - \prod\limits_{i = 1}^{n} {(1 - f(\dot{s}_{{a_{i} }} )^{\lambda } )^{{w_{i} }} } } \right)^{1/\lambda } } \right),\mathop {\hbox{min} }\limits_{1 \le i \le n} (\ddot{s}_{{b_{i} }} )} \right)} \right\}} .\)
First, let us consider \(\mathop {\lim }\limits_{\lambda \to 0} f^{ - 1} \left( {\left( {1 - \prod\limits_{i = 1}^{n} {(1 - f(\dot{s}_{{a_{i} }} )^{\lambda } )^{{w_{i} }} } } \right)^{1/\lambda } } \right)\). Based on the L’ Hopital’s rule, we can prove that
Therefore, the proof of Case (2) is completed.
1.3 The Proof of Theorem 2
Proof Since \(\hat{h}_{{S_{i} }} = \{ (\dot{s}_{a} ,\ddot{s}_{b} )\}\) for all i (i = 1, 2,…,n), then
1.4 The Proof of Theorem 3
Proof Since \(\dot{s}_{{a_{i} }} \le \dot{s}_{{c_{i} }}\) and \(\ddot{s}_{{b_{i} }} \le \ddot{s}_{{d_{i} }}\), for all i (i = 1, 2,…,n), we have
Then,
i.e.,
where l 1 and l 2 are the numbers of 2DLVs in \({\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }} ,\hat{h}_{{S_{2} }} , \ldots ,\hat{h}_{{S_{n} }} )\) and \({\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }}^{'} ,\hat{h}_{{S_{2} }}^{'} , \ldots ,\hat{h}_{{S_{n} }}^{'} )\), respectively.
-
1
If \(E ( {\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }} ,\hat{h}_{{S_{2} }} , \ldots ,\hat{h}_{{S_{n} }} )) < E({\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }}^{'} ,\hat{h}_{{S_{2} }}^{'} , \ldots ,\hat{h}_{{S_{n} }}^{'} ))\), according to the comparison laws of HF2DLEs, we can obtain \({\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }} ,\hat{h}_{{S_{2} }} , \ldots ,\hat{h}_{{S_{n} }} ) < {\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }}^{'} ,\hat{h}_{{S_{2} }}^{'} , \ldots ,\hat{h}_{{S_{n} }}^{'} ).\)
-
2
If \(E ( {\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }} ,\hat{h}_{{S_{2} }} , \ldots ,\hat{h}_{{S_{n} }} )){ = }E({\text{G-HF2DLWA}}(\hat{h}_{{S_{1} }}^{'} ,\hat{h}_{{S_{2} }}^{'} , \ldots ,\hat{h}_{{S_{n} }}^{'} ))\), since \(\dot{s}_{{a_{i} }} \le \dot{s}_{{c_{i} }}\) and \(\ddot{s}_{{b_{i} }} \le \ddot{s}_{{d_{i} }}\), for all i (i = 1, 2,…,n), then \(\dot{s}_{{a_{i} }} { = }\dot{s}_{{c_{i} }}\) and \(\ddot{s}_{{b_{i} }} { = }\ddot{s}_{{d_{i} }}\), i = 1, 2,…, n,
and thus,
i.e.,
Therefore, according to the comparison laws of HF2DLEs, we can obtain
To sum up, if \(\dot{s}_{{a_{i} }} \le \dot{s}_{{c_{i} }}\) and \(\ddot{s}_{{b_{i} }} \le \ddot{s}_{{d_{i} }}\), for all i (i = 1, 2,…,n), then
which completes the proof of Theorem 3.
1.5 The Proof of Theorem 4
Proof Since \(\dot{s}_{{a^{ - } }} = \mathop {\hbox{min} }\limits_{1 \le i \le n} \{ \dot{s}_{{a_{i} }} |(\dot{s}_{{a_{i} }} ,\ddot{s}_{{b_{i} }} ) \in \hat{h}_{{S_{i} }} \}\), \(\ddot{s}_{{b^{ - } }} = \mathop {\hbox{min} }\limits_{1 \le i \le n} \{ \ddot{s}_{{b_{i} }} |(\dot{s}_{{a_{i} }} ,\ddot{s}_{{b_{i} }} ) \in \hat{h}_{{S_{i} }} \}\), \(\dot{s}_{{a^{ + } }} = \mathop {\hbox{max} }\limits_{1 \le i \le n} \{ \dot{s}_{{a_{i} }} |(\dot{s}_{{a_{i} }} ,\ddot{s}_{{b_{i} }} ) \in \hat{h}_{{S_{i} }} \}\) and \(\ddot{s}_{{b^{ + } }} = \mathop {\hbox{max} }\limits_{1 \le i \le n} \{ \ddot{s}_{{b_{i} }} |(\dot{s}_{{a_{i} }} ,\ddot{s}_{{b_{i} }} ) \in \hat{h}_{{S_{i} }} \}\), we have \(\dot{s}_{{a^{ - } }} \le \dot{s}_{{a_{i} }} \le \dot{s}_{{a^{ + } }}\), \(\ddot{s}_{{b^{ - } }} \le \ddot{s}_{{b_{i} }} \le \ddot{s}_{{b^{ + } }}\), i = 1, 2,…,n.
Therefore, according to the monotonicity property of the G-HF2DLWA operator, we can obtain
which completes the proof of Theorem 4.
1.6 The Proof of Theorem 5
Proof Since ω = (ω 1, ω 2, …, ω n )T is a position weight vector, \((\hat{h}^{\prime}_{{S_{1} }} ,\hat{h}^{\prime}_{{S_{2} }} , \ldots ,\hat{h}^{\prime}_{{S_{n} }} )\) is any permutation of \((\hat{h}_{{S_{1} }} ,\hat{h}_{{S_{2} }} , \ldots ,\hat{h}_{{S_{n} }} )\), we have \(\hat{h}_{{S_{\sigma (i)} }}^{{}} = \hat{h}^{\prime}_{{S_{\sigma (i)} }}\), then
Thus,
i.e.,
which completes the proof of Theorem 9.
Rights and permissions
About this article
Cite this article
Liu, X., Ju, Y. & Qu, Q. Hesitant Fuzzy 2-Dimension Linguistic Term Set and its Application to Multiple Attribute Group Decision Making. Int. J. Fuzzy Syst. 20, 2301–2321 (2018). https://doi.org/10.1007/s40815-017-0384-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-017-0384-0