Abstract
Decision-making trial and evaluation laboratory (DEMATEL) method is comprehensively applied in the complicated socioeconomic system. Existing literatures on DEMATEL focus on static decision environment at single time node. However, due to the increasingly complex and rapidly changing socioeconomic environment, decision-making has become more complex and dynamic. In this paper, dynamic group DEMATEL method based on hesitant fuzzy linguistic term sets (HFLTSs) is proposed. In the view of the multi-stage, multi-factor and multi-expert complex decision-making situation, the method fully integrates the decision-making information of different periods from the perspective of dynamic evolution mechanism which can effectively link multi rounds DEMATEL decision-making process and reflect the time evolution of dynamic decision-making. Meanwhile, the TOPSIS method is applied to determine the weight of experts, which can effectively solve the influence of the best choice (positive ideal solution) and the worst choice (negative ideal solution) on the decision results in aggregation of group experts' preference information, and fully consider the influence of hesitation and fuzziness of expert preference representation on expert weight. Finally, a numerical comparison is given to verify validity and effectiveness of the proposed approach.
Similar content being viewed by others
References
Shieh, J.I., Wu, H., Huang, K.: A DEMATEL method in identifying key success factors of hospital service quality. Knowl. Based Syst. 23(9), 277–282 (2010)
Sujak, B., Shahadat, K., Kamrul, A., Shams, R.: Exploring the critical determinants of environmentally oriented public procurement using the DEMATEL method. J Environ. Manage. 225, 325–35 (2018)
Dharmalingam, R., Shivasankarappa, A., Neelamegam, A.: A novel approach for optimizing governance, risk management and compliance for enterprise information security using DEMATEL and FoM. Procedia Comput. Sci. 134, 365–370 (2018)
Gülçin, B., Sezin, G.: An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey. Int. J. Pro. Econ. 182, 435–448 (2016)
Du, Y.W., Zhou, W.: DSmT-based group DEMATEL method with reaching consensus. Group Decis. Negot. 28(6), 1201–1230 (2019)
Han, W., Sun, Y.H., Xie, H., Che, Z.M.: Hesitant fuzzy linguistic group DEMATEL method with multi-granular evaluation scales. Int. J. Fuzzy Syst. 20(7), 2187–2201 (2018)
Liu, H.B., Jiang, L., Martínez, L.: A dynamic multi-criteria decision making model with bipolar linguistic term sets. Expert Syst. Appl. 95, 104–112 (2018)
Baykasoğlua, A., Gölcük, İ: A dynamic multiple attribute decision making model with learning of fuzzy cognitive maps. Comput. Ind. Eng. 135, 1063–1076 (2019)
Yan, S., Liu, S., Liu, J., Wu, L.: Dynamic grey target decision making method with grey numbers based on existing state and future development trend of alternatives. J. Intell. Fuzzy Syst. 28(5), 2159–2168 (2015)
Rodríguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Transl. Fuzzy Syst. 20, 109–119 (2012)
Gao, Z.H., Li, M.Y., Gao, F., Wang, X.Y.: Fuzzy comprehensive evaluation on body parts’ weight coefficients towards sitting comfort based on AHP to limit entropy method. Math. Probl. Eng. 2019, 1–11 (2019)
Yang, X.J., Yan, L.L., Zeng, L.: How to handle uncertainties in AHP: the Cloud Delphi hierarchical analysis. Inf. Sci. 222(222), 384–404 (2013)
Zhao, M., Ren, R.R., Qiu, Y.H.: Experts’ weights method and computational experiment analysis based on intuitionistic fuzzy entropy measures. Control Decis. 30(7), 1233–1238 (2015)
Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)
Xu, Y.J., Wang, H.M.: Distance measure for linguistic decision making. Syst. Eng. Procedia 1, 450–456 (2011)
Zhu, B., Xu, Z.S., Xia, M.M.: Hesitant fuzzy geometric Bonferroni means. Inf. Sci. 205, 72–85 (2012)
Wei, C.P., Zhao, N., Tang, X.J.: Operators and comparisons of hesitant linguistic term sets. IEEE Trans. Fuzzy Syst. 22(3), 575–85 (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)
Rodríguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic term sets. Found. Intell. Syst. 122, 287–295 (2011)
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)
Xu, Z.S.: An approach based on similarity measure to multiple attribute decision making with trapezoid fuzzy linguistic variables. Fuzzy Syst. Knowl. Disc. 36(13), 110–117 (2005)
Xu, Z.S.: Deviation measures of linguistic preference relations in group decision making. Omega 33(3), 249–254 (2005)
Hung, W.L., Yang, M.S.: Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recogn. Lett. 25, 1603–1611 (2004)
Liao, H.C., Xu, Z.S.: A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optim. Decis. Mak. 12(4), 373–392 (2013)
Xu, Z.S., Xia, M.M.: Distance and similarity measures for hesitant fuzzy sets. Inf. Sci 181(11), 2128–2138 (2011)
Zhu, B., Xu, Z.S.: Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Trans. Fuzzy Syst. 22(1), 35–45 (2014)
Dutta, B., Singha, T., Goha, M., Lamata, M. T., Verdegay, J. L.: Post factum analysis in TOPSIS based decision making method. Expert Syst. Appl. 138. (2019)
Campanella, G., Ribeiro, R.A.: A framework for dynamic multiple-criteria decision making. Decis. Support Syst. 52(1), 52–60 (2011)
Acknowledgments
This research is supported by the Science Research Foundation of Yunnan Provincial Education Department of China (No. 2017ZZX164), and Yunnan Provincial Talent Training Project of China (No. 14119073).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xie, H., Ren, Q., Duan, W. et al. New Dynamic Group DEMATEL Decision-Making Method Based on Hesitant Fuzzy Linguistic Term Sets. Int. J. Fuzzy Syst. 23, 2118–2131 (2021). https://doi.org/10.1007/s40815-021-01081-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-021-01081-2