Cognitive Computation

, Volume 10, Issue 5, pp 737–751 | Cite as

A Projection-Based Outranking Method with Multi-Hesitant Fuzzy Linguistic Term Sets for Hotel Location Selection

  • Pu Ji
  • Hong-Yu Zhang
  • Jian-Qiang WangEmail author


Keen competition drives hotel companies to enhance their position. One way to do this is to select a proper hotel location. However, hotel location selection is a complex problem. This study establishes a multi-criteria hotel location selection method. In this method, cognitive information is depicted by multi-hesitant fuzzy linguistic term sets (MHFLTSs). Moreover, the method considers the non-compensation of criteria. It introduces the elimination and choice translating reality (ELECTRE) method. Notably, the method utilizes projection to define concordance and discordance indices. A case study and comparative study are performed in this study. They exhibit the feasibility of the method. Results of the studies show that the method can solve such problems, and they reveal the method’s advantages. One theoretical contribution lies in the characterization of cognitive information. MHFLTSs can handle vacillation of decision-makers caused by their complex cognition, and they express both conformity and divergence of opinions during cognitive processes. Our method has the advantages of the ELECTRE method. In addition, the ELECTRE method is improved by introducing the projection. The proposed method is promising in hotel location selection. Moreover, it is a potential option to address cognitive computation.


Multi-hesitant fuzzy linguistic term sets Multi-criteria decision-making Outranking method Projection Hotel location selection 



The authors are rather grateful to thank the editors and anonymous reviewers for their helpful comments and suggestions. This work was supported by the National Natural Science Foundation of China (nos. 71501192 and 71571193).

Compliance with Ethical Standards

All the authors have read and have abided by the statement of ethical standards for manuscripts. And we declare that:

(a) The material has not been published in whole or in part elsewhere;

(b) The paper is not currently being considered for publication elsewhere;

(c) All authors have been personally and actively involved in substantive work leading to the manuscript, and will hold themselves jointly and individually responsible for its content;

(d) Authors whose names appear on the submission have contributed sufficiently to the scientific work and therefore share collective responsibility and accountability for the results;

(e) All sources of funding of all the authors that may be relevant, including current funding of posts and funding for the research being reported;

(f) There is no conflict of interest.


  1. 1.
    Huskinson TLH, Haddock G. Individual differences in attitude structure: variance in the chronic reliance on affective and cognitive information. J Exp Soc Psychol. 2004;40(1):82–90.CrossRefGoogle Scholar
  2. 2.
    Liu P, Zhang X. A novel picture fuzzy linguistic aggregation operator and its application to group decision-making. Cogn Comput. 2017; Scholar
  3. 3.
    Zhao N, Xu Z, Liu F. Group decision making with dual hesitant fuzzy preference relations. Cogn Comput. 2016;8(6):1119–43.CrossRefGoogle Scholar
  4. 4.
    Liu Y, Zhang L, Deng P, He Z. Common subspace learning via cross-domain extreme learning machine. Cogn Comput. 2017;9(4):555–63.CrossRefGoogle Scholar
  5. 5.
    Thanh ND, Ali M, Le HS. A novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. Cogn Comput. 2017;9(4):526–44.CrossRefGoogle Scholar
  6. 6.
    Wootton AJ, Taylor SL, Day CR, Haycock PW. Optimizing echo state networks for static pattern recognition. Cogn Comput. 2017;9(3):391–9.CrossRefGoogle Scholar
  7. 7.
    Zhang HY, Ji P, Wang JQ, Chen XH. A neutrosophic normal cloud and its application in decision-making. Cogn Comput. 2016;8(4):649–69.CrossRefGoogle Scholar
  8. 8.
    Zadeh LA. Fuzzy sets. Inf Control. 1965;8(3):338–53.CrossRefGoogle Scholar
  9. 9.
    Peng HG, Zhang HY, Wang JQ. Cloud decision support model for selecting hotels on with probabilistic linguistic information. Int J Hosp Manag. 2018;68:124–138.CrossRefGoogle Scholar
  10. 10.
    Hu J, Yang Y, Zhang X, Chen X. Similarity and entropy measures for hesitant fuzzy sets. Int Trans Oper Res. 2018;25(3):857–86. Scholar
  11. 11.
    Liu P, Li H. Interval-valued intuitionistic fuzzy power Bonferroni aggregation operators and their application to group decision making. Cogn Comput. 2017;9(4):494–512.CrossRefGoogle Scholar
  12. 12.
    Farhadinia B. A multiple criteria decision making model with entropy weight in an interval-transformed hesitant fuzzy environment. Cogn Comput. 2017;9(4):513–25.CrossRefGoogle Scholar
  13. 13.
    Li J, Wang J. Multi-criteria outranking methods with hesitant probabilistic fuzzy sets. Cogn Comput. 2017;9(6):611–25.CrossRefGoogle Scholar
  14. 14.
    Tao Z, Han B, Chen H. On intuitionistic fuzzy copula aggregation operators in multiple-attribute decision making. Cogn Comput. 2018; Scholar
  15. 15.
    Chou TY, Hsu CL, Chen MC. A fuzzy multi-criteria decision model for international tourist hotels location selection. Int J Hosp Manag. 2008;27(2):293–301.CrossRefGoogle Scholar
  16. 16.
    Wibowo S. Interval-valued intuitionistic fuzzy multicriteria group decision making approach for hotel selection. Int J Mach Learn Comput. 2013;3(1):65–9.CrossRefGoogle Scholar
  17. 17.
    Yu SM, Wang J, Wang JQ, Li L. A multi-criteria decision-making model for hotel selection with linguistic distribution assessments. Appl Soft Comput. 2017; Scholar
  18. 18.
    Wang J, J-q W, Z-p T, D-y Z. A multi-hesitant fuzzy linguistic multi-criteria decision-making approach for logistics outsourcing with incomplete weight information. Int Trans Oper Res. 2018;25(3):831–56.CrossRefGoogle Scholar
  19. 19.
    Rodriguez RM, MartíNez L, Herrera F. Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst. 2012;20(1):109–19.CrossRefGoogle Scholar
  20. 20.
    Zhang Z, Wu C. Hesitant fuzzy linguistic aggregation operators and their applications to multiple attribute group decision making. J Intell Fuzzy Syst. 2014;26(5):2185–202.Google Scholar
  21. 21.
    Wibowo S, Deng H. A fuzzy multi-criteria group decision making approach for hotel location evaluation and selection. Lect Notes Electr Eng. 2012;107:1599–608.CrossRefGoogle Scholar
  22. 22.
    Yang Y, Wong KKF, Wang T. How do hotels choose their location? Evidence from hotels in Beijing. Int J Hosp Manag. 2012;31(3):675–85.CrossRefGoogle Scholar
  23. 23.
    Pan CM. Market structure and profitability in the international tourist hotel industry. Tourism Manage. 2005;26(6):845–50.CrossRefGoogle Scholar
  24. 24.
    Khalili S, Zaremehrjerdi Y, Fallahnezhad MS, Mohammadzade H. Hotel location problem using Erlang queuing model under uncertainty. Int J Eng. 2014;27(12):1879–87.Google Scholar
  25. 25.
    Jiang Y, Jin S, Peng J. Hierarchical fuzzy rule interpolation and its application for hotels location selection. Int J Cogn Informatics Natural Intell. 2016;10(1):55–79.CrossRefGoogle Scholar
  26. 26.
    Wang J, Wang JQ, Zhang HY, Chen XH. Multi-criteria group decision-making approach based on 2-tuple linguistic aggregation operators with multi-hesitant fuzzy linguistic information. Int J Fuzzy Syst. 2016;18(1):81–97.CrossRefGoogle Scholar
  27. 27.
    Zhou H, Wang J, Zhang H. Stochastic multi-criteria decision-making approach based on SMAA-ELECTRE with extended gray numbers. Int Trans Oper Res. 2016;
  28. 28.
    Roy B. Electre III: un algorithme de classements fondé sur une représentation floue des préférences en présence de critères multiples. Cahiers Centre Études Rech Opér. 1978;20(1):3–24.Google Scholar
  29. 29.
    Vahdani B, Hadipour H. Extension of the ELECTRE method based on interval-valued fuzzy sets. Soft Comput. 2011;15(15):569–79.CrossRefGoogle Scholar
  30. 30.
    Chen TY. An ELECTRE-based outranking method for multiple criteria group decision making using interval type-2 fuzzy sets. Inf Sci. 2014;263(3):1–21.Google Scholar
  31. 31.
    Leyva-López JC, Fernández-González E. A new method for group decision support based on ELECTRE III methodology. Eur J Oper Res. 2003;148(1):14–27.CrossRefGoogle Scholar
  32. 32.
    Wu Y, Zhang J, Yuan J, Geng S, Zhang H. Study of decision framework of offshore wind power station site selection based on ELECTRE-III under intuitionistic fuzzy environment: a case of China. Energy Conv Manag. 2016;113:66–81.CrossRefGoogle Scholar
  33. 33.
    Hashemi SS, Hajiagha SHR, Zavadskas EK, Mahdiraji HA. Multicriteria group decision making with ELECTRE III method based on interval-valued intuitionistic fuzzy information. Appl Math Model. 2016;40(2):1554–64.CrossRefGoogle Scholar
  34. 34.
    Rashid T, Faizi S, Xu Z, Zafar S. ELECTRE-based outranking method for multi-criteria decision making using hesitant intuitionistic fuzzy linguistic term sets. Int J Fuzzy Syst. 2018;20(1):78–92.CrossRefGoogle Scholar
  35. 35.
    Gitinavard H, Ghaderi H, Pishvaee MS. Green supplier evaluation in manufacturing systems: a novel interval-valued hesitant fuzzy group outranking approach. Soft Comput. 2017; Scholar
  36. 36.
    Wang JQ, Peng JJ, Zhang HY, Chen XH. Outranking approach for multi-criteria decision-making problems with hesitant interval-valued fuzzy sets. Soft Comput. 2017;
  37. 37.
    Chavira DAG, Lopez JCL, Noriega JJS, Valenzuela OA, Carrillo PAA. A credit ranking model for a parafinancial company based on the ELECTRE-III method and a multiobjective evolutionary algorithm. Appl Soft Comput. 2017;60:190–201.CrossRefGoogle Scholar
  38. 38.
    Vezmelai AS, Lashgari Z, Keyghobadi A. Portfolio selection using ELECTRE III: evidence from Tehran stock exchange. Decis Sci Lett. 2015;4(2):227–36.CrossRefGoogle Scholar
  39. 39.
    Montazer GA, Saremi HQ, Ramezani M. Design a new mixed expert decision aiding system using fuzzy ELECTRE III method for vendor selection. Expert Syst Appl. 2009;36(8):10837–47.CrossRefGoogle Scholar
  40. 40.
    Xu Z. On method for uncertain multiple attribute decision making problems with uncertain multiplicative preference information on alternatives. Fuzzy Optim Decis Mak. 2005;4(2):131–9.CrossRefGoogle Scholar
  41. 41.
    Yue Z, Jia Y. A direct projection-based group decision-making methodology with crisp values and interval data. Soft Comput. 2017;21(9):2395–405.CrossRefGoogle Scholar
  42. 42.
    Zeng S, Bale Entis T, Chen J, Luo G. A projection method for multiple attribute group decision making with intuitionistic fuzzy information. Informatica. 2013;24(3):485–503.Google Scholar
  43. 43.
    Zhang X, Jin F, Liu P. A grey relational projection method for multi-attribute decision making based on intuitionistic trapezoidal fuzzy number. Appl Math Model. 2013;37(5):3467–77.CrossRefGoogle Scholar
  44. 44.
    Ye J. Bidirectional projection method for multiple attribute group decision making with neutrosophic numbers. Neural Comput Appl. 2017;28(5):1021–9.CrossRefGoogle Scholar
  45. 45.
    Xu GL, Liu F. An approach to group decision making based on interval multiplicative and fuzzy preference relations by using projection. Appl Math Model. 2013;37(6):3929–43.CrossRefGoogle Scholar
  46. 46.
    Ji P, Zhang H, Wang J. A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection. Neural Comput Appl. 2018;29(1):221–34.CrossRefGoogle Scholar
  47. 47.
    Adam I, Amuquandoh FE. Dimensions of hotel location in the Kumasi metropolis, Ghana. Tour Manag Perspect. 2013;8:1–8.CrossRefGoogle Scholar
  48. 48.
    Youngthelin L, Boluk K. A case study of human resource practices in small hotels in Sweden. J Hum Resour Hosp Tour. 2012;11(4):327–53.CrossRefGoogle Scholar
  49. 49.
    Lee LW, Chen SM. Fuzzy decision making based on hesitant fuzzy linguistic term sets. 5th Asian Conference on Intelligent Information and Database Systems, Kuala Lumpur, Malaysia. Springer-Verlag Berlin, Heidelberg; 2013. p 21–30.CrossRefGoogle Scholar
  50. 50.
    Wang JQ, Wang J, Chen QH, Zhang HY, Chen XH. An outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets. Inf Sci. 2014;280:224–36.Google Scholar
  51. 51.
    Tian ZP, Wang J, Wang JQ, Zhang HY. A likelihood-based qualitative flexible approach with hesitant fuzzy linguistic information. Cogn Comput. 2016;8(4):670–83.CrossRefGoogle Scholar
  52. 52.
    Wang J, Wang JQ, Zhang HY. A likelihood-based TODIM approach based on multi-hesitant fuzzy linguistic information for evaluation in logistics outsourcing. Comput Ind Eng. 2016;99(C:287–99.CrossRefGoogle Scholar
  53. 53.
    Li X, Chen X. D-intuitionistic hesitant fuzzy sets and their application in multiple attribute decision making. Cogn Comput. 2018; Scholar
  54. 54.
    Yu SM, Wang J, Wang JQ. An extended TODIM approach with intuitionistic linguistic numbers. Int Trans Oper Res. 2018;25(3):781–805.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of BusinessCentral South UniversityChangshaChina

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