Multiple Criteria Group Decision-Making Based on Hesitant Fuzzy Linguistic Consensus Model for Fashion Sales Forecasting

  • Ming Tang
  • Huchang LiaoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)


In many real-world multiple criteria group decision-making process, people cannot provide accurate preference information over a set of alternatives because of the increasingly complex environment. Fashion sales forecasting can be taken as a multi-criteria group decision-making problem given that people need to consider product life cycle, year-on-year growth rate, seasonal factor, industry factor, and consumer factor comprehensively when they forecast the fashion scales. In this paper, we developed a fuzzy linguistic model for fashion sales forecasting. Approaches such as hesitant fuzzy linguistic preference relation and ordinal consensus measure are used in our paper. Decision-makers compare alternatives over each criterion and the ranking of alternatives can be derived. Based on the ranking provided by the decision-makers, we introduce the ordinal consensus of the group. Then, a consensus reaching process is given to raise the degree of consensus.


Multiple criteria group decision-making Fashion sales forecasting Hesitant fuzzy linguistic preference relation Ordinal consensus 



The work was supported by the National Natural Science Foundation of China (71501135, 71771156), the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23), the 2018 Key Project of the Key Research Institute of Humanities and Social Sciences in Sichuan Province (No. LYC18-02, No. DSWL18-2), and the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23).


  1. 1.
    Kuo, R.J., Xue, K.C.: A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights. Decis. Support Syst. 24, 105–126 (1998). Scholar
  2. 2.
    Choi, T.M., Chow, P.S.: Mean-variance analysis of quick response program. Int. J. Prod. Econ. 114, 456–475 (2008). Scholar
  3. 3.
    Liu, W.S., Liao, H.C.: A bibliometric analysis of fuzzy decision research during 1970–2015. Int. J. Fuzzy Syst. 19, 1–14 (2017). Scholar
  4. 4.
    Adnan, M.R.H.M., Sarkheyli, A., Zain, A.M., Haron, H.: Fuzzy logic for modeling machining process: a review. Artif. Intelli. Rev. 43, 345–379 (2015). Scholar
  5. 5.
    Rodríguez, R.M., Martínez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20, 109–119 (2012). Scholar
  6. 6.
    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). Scholar
  7. 7.
    Liao, H.C., Yang, L.Y., Xu, Z.S.: Two new approaches based on ELECTRE II to solve the multiple criteria decision making problems with hesitant fuzzy linguistic term sets. Appl. Soft Comput. 63, 223–234 (2018). Scholar
  8. 8.
    Liao, H.C., Xu, Z.S., Enrique, H.V., Herrera, F.: Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the art survey. Int. J. Fuzzy Syst. (2018). Scholar
  9. 9.
    Rodríguez, R.M., Martínez, L., Herrera, F.: A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Inform. Sci. 241, 28–42 (2013). Scholar
  10. 10.
    Liao, H.C., Xu, Z.S., Zeng, X.J., Merigó, J.M.: Framework of group decision making with intuitionistic fuzzy preference information. IEEE Trans. Fuzzy Syst. 23, 1211–1227 (2015). Scholar
  11. 11.
    Wu, Z.B., Xu, J.P.: Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations. Omega 65, 28–40 (2016). Scholar
  12. 12.
    Wu, Z.B., Xu, J.P.: An interactive consensus reaching model for decision making under hesitation linguistic environment. J. Intell. Fuzzy Syst. 31, 1635–1644 (2016). Scholar
  13. 13.
    Liao, H.C., Xu, Z.S., Zeng, X.J., Merigó, J.M.: Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowle. Based Syst. 76, 127–138 (2015). Scholar
  14. 14.
    Zhu, B., Xu, Z.S.: Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Trans. Fuzzy Syst. 22, 35–45 (2014). Scholar
  15. 15.
    Saint, S., Lawson, J.R.: Rules for reaching consensus: a modern approach to decision making. Jossey-Bass, San Francisco (1994)Google Scholar
  16. 16.
    Xu, Y.J., Wang, H.M.: A group consensus decision support model for hesitant 2-tuple fuzzy linguistic preference relations with additive consistency. J. Intell. Fuzzy Syst. 33, 41–54 (2017). Scholar
  17. 17.
    Cook, W.D.: Distance-based and ad hoc consensus models in ordinal preference ranking. Eur. J. Oper. Res. 172, 369–385 (2006). Scholar
  18. 18.
    Xia, M., Zhang, Y.C., Weng, L.G., Ye, X.L.: Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs. Knowle. Based Syst. 36, 253–259 (2012). Scholar
  19. 19.
    Chang, P.C., Wang, Y.W.: Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert Syst. Appl. 30, 715–726 (2006). Scholar
  20. 20.
    Sun, Z.L., Choi, T.M., Au, K.F., Yu, Y.: Sales forecasting using extreme learning machine with applications in fashion retailing. Decis. Support Syst. 46, 411–419 (2008). Scholar
  21. 21.
    Lin, C.T., Lee, I.F.: Artificial intelligence diagnosis algorithm for expanding a precision expert forecasting system. Expert Syst. Appl. 36, 8385–8390 (2009). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Business SchoolSichuan UniversityChengduChina

Personalised recommendations