A GDM Method Based on Granular Computing for Academic Library Management

  • Francisco Javier Cabrerizo
  • Raquel Ureña
  • Juan Antonio Morente-Molinera
  • Enrique Herrera-Viedma
Part of the Studies in Big Data book series (SBD, volume 10)


An academic library, as a service organization, has to maintain a level of service quality that, satisfying its users, will assure funding for its existence and development. To do so, the general manager, which is in charge of distributing the funding, asks to the staff of the library about their opinions in the allocation of the budget. An important issue here is the level of agreement achieved among the staff before making a decision. In this paper,we propose a group decision making method based on granular computing aiding to the general manager to decide about funding distribution according to the staff’s opinions. Assuming fuzzy preference relations to represent the preferences of the staff, a concept of a granular fuzzy preference relation is developed, where each pairwise comparison is formed as an information granule instead of a single numeric value. It offers the required flexibility to increase the level of agreement within the staff, using the fact that the most suitable numeric representative of the fuzzy preference relation is selected.


Group decision making Academic library Granular computing Consensus 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alonso, S., Cabrerizo, F.J., Chiclana, F., Herrera, F., Herrera-Viedma, E.: An interactive decision support system based on consistency criteria. Journal of Multiple-Valued Logic & Soft Computing 14(3-5), 371–385 (2008)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Alonso, S., Pérez, I.J., Cabrerizo, F.J., Herrera-Viedma, E.: A linguistic consensus model for web 2.0 communities. Applied Soft Computing 13(1), 149–157 (2013)CrossRefGoogle Scholar
  3. 3.
    Baalhavaeji, F., Isfandyari-Moghaddam, A., Aqili, S.V., Shakooii, A.: Quality assessment of academic libraries’ performance with a special reference to information technology-based services: Suggesting an evaluation checklist. The Electronic Library 28(4), 592–621 (2010)CrossRefGoogle Scholar
  4. 4.
    Bargiela, A.: Interval and ellipsoidal uncertainty models. In: Pedrycz, W. (ed.) Granular Computing: An Emerging Paradigm, pp. 23–57. Physica-Verlag (2001)Google Scholar
  5. 5.
    Bokos, G.D.: From the “diffusion” of functions to the “recomposition” of the role: The future of the academic libraries in the context of educational and research. In: Proceedings of the 8th Panhellenic Conference of Academic Libraries, pp. 46–56 (1999)Google Scholar
  6. 6.
    Butler, C.T., Rothstein, A.: On conflict and consensus: A handbook on formal consensus decision making. Tahoma Partk (2006)Google Scholar
  7. 7.
    Cabrerizo, F.J., Heradio, R., Pérez, I.J., Herrera-Viedma, E.: A selection process based on additive consistency to deal with incomplete fuzzy linguistic information. Journal of Universal Computer Science 16(1), 62–81 (2010)zbMATHMathSciNetGoogle Scholar
  8. 8.
    Cabrerizo, F.J., Moreno, J.M., Pérez, I.J., Herrera-Viedma, E.: Analyzing consensus approaches in fuzzy group decision making: Advantages and drawbacks. Soft Computing 14(5), 451–463 (2010)CrossRefGoogle Scholar
  9. 9.
    Cabrerizo, F.J., Pérez, I.J., Herrera-Viedma, E.: Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information. Knowledge-Based Systems 23(2), 169–181 (2010)CrossRefGoogle Scholar
  10. 10.
    Chen, S.J., Hwang, C.L.: Fuzzy multiple attributive decision making: Theory and its applications. Springer, Berlin (1992)CrossRefGoogle Scholar
  11. 11.
    Chiclana, F., Herrera, F., Herrera-Viedma, E., Poyatos, M.C.: A classification method of alternatives of multiple preference ordering criteria based on fuzzy majority. The Journal of Fuzzy Mathematics 4(4), 801–813 (1996)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Chiclana, F., Herrera, F., Herrera-Viedma, E.: Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations. Fuzzy Sets and Systems 97(1), 33–48 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Chiclana, F., Herrera, F., Herrera-Viedma, E.: A note on the internal consistency of various preference representations. Fuzzy Sets and Systems 131(1), 75–78 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Chiclana, F., Mata, F., Martínez, L., Herrera-Viedma, E., Alonso, S.: Integration of a consistency control module within a consensus model. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 16(1), 35–53 (2008)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Cook, C., Heath, F.M.: Users’ perception of library service quality: A LibQUAL+ qualitative study. Library Trends 49(4), 548–584 (2001)Google Scholar
  16. 16.
    Cutello, V., Montero, J.: Fuzzy rationality measures. Fuzzy Sets and Systems 62(1), 39–54 (1994)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Daneshyari, M., Yen, G.G.: Constrained multiple-swarm particle swarm optimization within a cultural framework. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 42(2), 475–490 (2012)CrossRefGoogle Scholar
  18. 18.
    Fodor, J., Roubens, M.: Fuzzy preference modelling and multicriteria decision support. Kluwer, Dordrecht (1994)CrossRefzbMATHGoogle Scholar
  19. 19.
    Fu, Y., Ding, M., Zhou, C.: Angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 42(2), 511–526 (2012)CrossRefGoogle Scholar
  20. 20.
    Grigoriadou, G., Kipourou, A., Mouratidis, E., Theodoridou, M.: Digital academic libraries: An important tool in engineering education. In: Proceedings of the 7th Baltic Region Seminar on Engineering Education, pp. 41–44 (2003)Google Scholar
  21. 21.
    Heradio, R., Cabrerizo, F.J., Fernández-Amorós, D., Herrera, M., Herrera-Viedma, E.: A fuzzy linguistic model to evaluate the quality of library 2.0 functionalities. International Journal of Information Management 33(4), 642–654 (2013)CrossRefGoogle Scholar
  22. 22.
    Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A model of consensus in group decision making under linguistic assessments. Fuzzy Sets and Systems 78(1), 73–87 (1996)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A rational consensus model in group decision making using linguistic assessments. Fuzzy Sets and Systems 88(1), 31–49 (1997)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: Linguistic measures based on fuzzy coincidence for reaching consensus in group decision making. International Journal of Approximate Reasoning 16(3-4), 309–334 (1997)CrossRefzbMATHGoogle Scholar
  25. 25.
    Herrera-Viedma, E., Herrera, F., Alonso, S.: Group decision-making model with incomplete fuzzy preference relations based on additive consistency. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics 37(1), 176–189 (2007)CrossRefGoogle Scholar
  26. 26.
    Herrera-Viedma, E., Herrera, F., Chiclana, F., Luque, M.: Some issues on consistency of fuzzy preference relations. European Journal of Operational Research 154(1), 98–109 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  27. 27.
    Herrera-Viedma, E., López-Gijón, J.: Libraries’ social role in the information age. Science 339(6126), 1382 (2013)CrossRefGoogle Scholar
  28. 28.
    Herrera-Viedma, E., Cabrerizo, F.J., Kacprzyk, J., Pedrycz, W.: A review of soft consensus models in a fuzzy environment. Information Fusion 17, 4–13 (2014)CrossRefGoogle Scholar
  29. 29.
    Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets and Systems 18(2), 105–118 (1986)CrossRefzbMATHMathSciNetGoogle Scholar
  30. 30.
    Kacprzyk, J., Fedrizzi, M., Nurmi, H.: Group decision making and consensus under fuzzy preferences and fuzzy majority. Fuzzy Sets and Systems 49(1), 21–31 (1992)CrossRefzbMATHMathSciNetGoogle Scholar
  31. 31.
    Kacprzyk, J., Zadrozny, S.: Soft computing and web intelligence for supporting consensus reaching. Soft Computing 14(8), 833–846 (2010)CrossRefGoogle Scholar
  32. 32.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  33. 33.
    Mata, F., Martínez, F., Herrera-Viedma, E.: An adaptive consensus support model for group decision making problems in a multi-granular fuzzy linguistic context. IEEE Transactions on Fuzzy Systems 17(2), 279–290 (2009)CrossRefGoogle Scholar
  34. 34.
    Orlovski, S.A.: Decision-making with a fuzzy preference relation. Fuzzy Sets and Systems 1(3), 155–167 (1978)CrossRefMathSciNetGoogle Scholar
  35. 35.
    Pedrycz, A., Hirota, K., Pedrycz, W., Dong, F.: Granular representation and granular computing with fuzzy sets. Fuzzy Sets and Systems 203, 17–32 (2012)CrossRefMathSciNetGoogle Scholar
  36. 36.
    Pedrycz, W.: The principle of justifiable granularity and an optimization of information granularity allocation as fundamentals of granular computing. Journal of Information Processing Systems 7(3), 397–412 (2011)CrossRefGoogle Scholar
  37. 37.
    Pedrycz, W.: Granular computing: Analysis and design of intelligent systems. CRC Press/Francis Taylor, Boca Raton (2013)CrossRefGoogle Scholar
  38. 38.
    Pedrycz, W.: Knowledge management and semantic modeling: A role of information granularity. International Journal of Software Engineering and Knowledge 23(1), 5–12 (2013)CrossRefMathSciNetGoogle Scholar
  39. 39.
    Pérez, I.J., Cabrerizo, F.J., Alonso, S., Herrera-Viedma, E.: A new consensus model for group decision making problems with non homogeneous experts. IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(4), 494–498 (2014)CrossRefGoogle Scholar
  40. 40.
    Roubens, M.: Fuzzy sets and decision analysis. Fuzzy Sets and Systems 90(2), 199–206 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  41. 41.
    Saint, S., Lawson, J.R.: Rules for reaching consensus: A moderm approach to decision making. Jossey-Bass (1994)Google Scholar
  42. 42.
    Słowiński, R., Greco, S., Matarazzo, B.: Rough set analysis of preference-ordered data. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 44–59. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  43. 43.
    Szmidt, E., Kacprzyk, J.: A consensus-reaching process under intuitionistic fuzzy preference relations. International Journal of Intelligent Systems 18(7), 837–852 (2003)CrossRefzbMATHGoogle Scholar
  44. 44.
    Tanino, T.: Fuzzy preference orderings in group decision making. Fuzzy Sets and Systems 12(2), 117–131 (1984)CrossRefzbMATHMathSciNetGoogle Scholar
  45. 45.
    Tsekouras, G.E., Tsimikas, J.: On training RBF neural networks using input-output fuzzy clustering and particle swarm optimization. Fuzzy Sets and Systems 221, 65–89 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  46. 46.
    Wiener, S.A.: Library quality and impact: Is there a relationship between new measures and traditional measures? Journal of Academic Librarianship 31(5), 432–437 (2005)CrossRefMathSciNetGoogle Scholar
  47. 47.
    Xu, Z.S.: An automatic approach to reaching consensus in multiple attribute group decision making. Computers & Industrial Engineering 56(4), 1369–1374 (2009)CrossRefGoogle Scholar
  48. 48.
    Xu, Z.S.: Consistency of interval fuzzy preference relations in group decision making. Applied Soft Computing 11(5), 3898–3909 (2011)CrossRefGoogle Scholar
  49. 49.
    Xu, Z.S., Cai, X.: Group consensus algorithms based on preference relations. Information Sciences 181(1), 150–162 (2011)CrossRefzbMATHGoogle Scholar
  50. 50.
    Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems Man and Cybernetics 18(1), 183–190 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  51. 51.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)CrossRefzbMATHMathSciNetGoogle Scholar
  52. 52.
    Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning. Part I. Information Sciences 8(3), 199–243 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  53. 53.
    Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning. Part II. Information Sciences 8(4), 301–357 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  54. 54.
    Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning. Part III. Information Sciences 9(1), 43–80 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  55. 55.
    Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications 9(1), 149–184 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  56. 56.
    Zadeh, L.A.: Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. Journal of Statistical Planning and Inference 105(1), 233–264 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  57. 57.
    Zhang, G., Dong, Y., Xu, Y., Li, H.: Minimum-cost consensus models under aggregation operators. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 41(6), 1253–1261 (2011)CrossRefGoogle Scholar
  58. 58.
    Zhang, G., Dong, Y., Xu, Y.: Linear optimization modeling of consistency issues in group decision making based on fuzzy preference relations. Expert Systems with Applications 39(3), 2415–2420 (2012)CrossRefMathSciNetGoogle Scholar
  59. 59.
    Zhang, Y., Wang, S., Phillips, P., Ji, G.: Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Systems 64, 22–31 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Francisco Javier Cabrerizo
    • 1
  • Raquel Ureña
    • 2
  • Juan Antonio Morente-Molinera
    • 2
  • Enrique Herrera-Viedma
    • 2
  1. 1.Department of Software Engineering and Computer SystemsUniversidad Nacional de Educación a Distancia (UNED)MadridSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

Personalised recommendations