Modeling and Simulating for the Treatment of Subjectivity in the Process of Choosing Personnel Using Fuzzy Logic

  • Noel Varela Izquierdo
  • Mercedes Gaitan
  • Omar Bonerge Pineda Lezama
  • Nelson Alberto Lizardo Zelaya
  • Jesus SilvaEmail author
  • Roberto Rene Moreno Garcia
  • Rafael Gomez Dorta
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)


Every day organizations pay more attention to Human Resources Management, because the human factor is preponderant in the results of it. One of the important policies is the Selection of Personnel, these are needed for their decision-making results, which in many organizations is done in a subjective manner and which brings consequences not very favorable to them. Taking this problem into account, it is decided to design and apply procedures and tools of fuzzy mathematics to reduce subjectivity and uncertainty in decision-making, creating work algorithms for this policy that includes multifactorial weights and analysis with measurement indicators that they allow tangible and reliable results. In this case of personnel selection, eight candidates were taken into account and by applying a diffuse evaluation system, the candidate with the highest rating of 98% was chosen. This indicates that subjectivity was reduced when choosing the best evaluated candidate.


Personnel selection Fuzzy mathematics Diffuse evaluation system 


  1. 1.
    Cuesta, A.: Tecnología de Gestión de Recursos Humanos (Tercera Edición). La Habana, Editorial Félix Varela (2010). ISBN 9789590713415Google Scholar
  2. 2.
    Gallego, M.: Gestión humana basada en competencias contribución efectiva al logro de los objetivos organizacionales. Revista universidad EAFIT 36(119), 63–71 (2012)Google Scholar
  3. 3.
    Varela, N., Fernández, D., Pineda, O., Viloria, A.: Selection of the best regression model to explain the variables that influence labor accident case electrical company. J. Eng. Appl. Sci. 12, 2956–2962 (2017)Google Scholar
  4. 4.
    Zhang, S., Liu, S.: A GRA based intuitionistic fuzzy multicriteria group decision making method for personnel selection. Expert Syst. Appl. 38(9), 11401–11405 (2011)CrossRefGoogle Scholar
  5. 5.
    Domínguez, L.A.P., Iniesta, A.A., Alcaraz, J.L.G., Rosales, D.J.V.: Análisis Dimensional Difuso Intuicionista para la Selección de. Practitioner 1, 9 (2015)Google Scholar
  6. 6.
    Baležentis, A., Baležentis, T., Brauers, W.K.M.: Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Syst. Appl. 39, 7961–7967 (2012)CrossRefGoogle Scholar
  7. 7.
    Chai, J., Liu, J.N.K., Ngai, E.W.T.: Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Syst. Appl. 40, 3872–3885 (2013)CrossRefGoogle Scholar
  8. 8.
    Afshari, A.R., Yusuff, R.M., Derayatifar, A.R.: Linguistic extension of fuzzy integral for group personnel selection problem. Arabian J. Sci. Eng. 38(10), 2901–2910 (2013)CrossRefGoogle Scholar
  9. 9.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  10. 10.
    Kelemenis, A., Askounis, D.: A new TOPSIS-based multicriteria approach to personnel selection. Expert Syst. Appl. 37(7), 4999–5008 (2010)CrossRefGoogle Scholar
  11. 11.
    Canós, L., Casasús, T., Crespo, E., Lara, T., Pérez, J.: Personnel selection based on fuzzy methods. Revista de Matemática: Teoría y Aplicaciones 18(1), 177–192 (2011)zbMATHGoogle Scholar
  12. 12.
    Dursun, M., Karsak, E.E.: A fuzzy MCDM approach for personnel selection. Expert Syst. Appl. 37(6), 4324–4330 (2010)CrossRefGoogle Scholar
  13. 13.
    Alliger, G.M., Feinzig, S.L., Janak, E.: Fuzzy sets and personnel selection: discussion and an application. J. Occup. Organ. Psychol. 66, 163–169 (1993)CrossRefGoogle Scholar
  14. 14.
    Licata, I.: General system theory, like-quantum semantics and fuzzy sets. In: Minati, G., Abram, M. (eds.) System of Emergence, Research and Development, pp. 724–734. Springer, New York (2006). Scholar
  15. 15.
    Zadeh, L.A.: Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. J. Stat. Plan. Inference 105, 233–264 (2002)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kabak, M., Burmaoglu, S., Kazancoglu, Y.: A fuzzy hibrid MCDM approach for professional selection. Expert Syst. Appl. 39, 3516–3525 (2012)CrossRefGoogle Scholar
  17. 17.
    Zadeh, L.A.: Toward extended fuzzy logic. A first step. Fuzzy Sets Syst. 160, 3175–3181 (2009)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Özdaban, I., Özkan, C.: A case study on evaluating personnel and jobs jointly with fuzzy distances. Int. J. Ind. Eng. 18(4), 169–179 (2011)Google Scholar
  19. 19.
    Izquierdo, N.V., Viloria, A., Lezama, O.B.P., Gaitán-Angulo, M., Herrera, H.H.: Performance evaluation by means of fuzzy mathematics. The case of a clinical laboratory. J. Control Theory Appl. (2016). ISSN 0974-5572Google Scholar
  20. 20.
    Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10942, pp. 164–173. Springer, Cham (2018). Scholar
  21. 21.
    Viloria, A.: Commercial strategies providers pharmaceutical chains for logistics cost reduction. Indian J. Sci. Technol. 8(1), 1–6 (2016)Google Scholar
  22. 22.
    Viloria, A., Wichez, M., Acuna, N.: Turnover increased Massive Consumer Products through the Implementation of Design Standards based on the Buyer. Indian J. Sci. Technol. 9(46), 35–42 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Noel Varela Izquierdo
    • 1
  • Mercedes Gaitan
    • 2
  • Omar Bonerge Pineda Lezama
    • 3
  • Nelson Alberto Lizardo Zelaya
    • 3
  • Jesus Silva
    • 4
    Email author
  • Roberto Rene Moreno Garcia
    • 5
  • Rafael Gomez Dorta
    • 6
  1. 1.Universidad de la Costa (CUC)BaranquillaColombia
  2. 2.Corporación Universitaria Empresarial de Salamanca – CUESBarranquillaColombia
  3. 3.Universidad Tecnologica Centroamericana (UNITEC)San Pedro SulaHonduras
  4. 4.Universidad Peruana de Ciencias AplicdasLimaPeru
  5. 5.Universidad de OrienteSantiago de CubaCuba
  6. 6.BecamoVillanuevaHonduras

Personalised recommendations