A Fuzzy AHP–TOPSIS-Based Group Decision-Making Approach to IT Personnel Selection

  • Funda Samanlioglu
  • Yunus Emre Taskaya
  • Utku Can Gulen
  • Ogulcan Cokcan
Article
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Abstract

Global competition and the rapid development of information technologies force organizations to continuously change their ways. Nowadays, organizations need personnel who make a difference through innovative ideas and who keep up with the rapid changes. In this paper, the personnel selection process in a Turkish dairy company’s information technology (IT) department is discussed as a group multi-criteria decision-making problem. The main purpose of the paper is to select the best employee candidate for an IT department by integrating fuzzy analytic hierarchy process (fuzzy AHP) with Chang’s (Eur J Oper Res 95(3):649–655, 1996) extent analysis and fuzzy The Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS). Decision makers’ (DMs) verbal evaluations are included in the process using intuitionistic fuzzy numbers. In fuzzy AHP–TOPSIS calculations, during the group decision-making process, hierarchical level weights, reflecting the importance of DMs’ verbal evaluations, are utilized. First, with fuzzy AHP, the importance weights of thirty sub-criteria are determined, and then, with fuzzy TOPSIS five IT personnel alternatives are ranked utilizing the weights obtained with fuzzy AHP.

Keywords

Personnel selection Fuzzy AHP Fuzzy TOPSIS Group decision making Hierarchical level weights 
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References

  1. 1.
    Ayhan, M.B.: A Fuzzy AHP Approach for supplier selection problem: a case study in a gear motor company. Int. J. Manag. Value Supply Chains 4(3), 11 (2013)CrossRefGoogle Scholar
  2. 2.
    Alguliyev, R.M., Aliguliyev, R.M., Mahmudova, R.S.: Multicriteria personnel selection by the modified fuzzy VIKOR method. Sci. World J. Article ID 612767, 1–16 (2015)Google Scholar
  3. 3.
    Amiri, M.P.: Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods. Expert Syst. Appl. 37(9), 6218–6224 (2010)CrossRefGoogle Scholar
  4. 4.
    Avikala, S., Mishraa, P., Jain, R.: A Fuzzy AHP and PROMETHEE method-based heuristic for disassembly line balancing problems. Int. J. Prod. Res. 52(5), 1306–1317 (2014)CrossRefGoogle Scholar
  5. 5.
    Balezentis, A., Balezentis, T., Brauers, W.K.: Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Syst. Appl. 39(9), 7961–7967 (2012)CrossRefMATHGoogle Scholar
  6. 6.
    Boran, F.E., Genc, S., Akay, D.: Personnel selection based on intuitionistic fuzzy sets. Hum. Factors Ergonomics Manuf. Serv. Ind. 21(5), 493–503 (2011)CrossRefGoogle Scholar
  7. 7.
    Bozbura, F.T., Beskese, A.: Prioritization of organizational capital measurement indicators using fuzzy AHP. Int. J. Approx. Reason. 44(2), 124–147 (2007)CrossRefGoogle Scholar
  8. 8.
    Büyüközkan, G., Çifçi, G.: A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst. Appl. 39(3), 2341–2354 (2012)CrossRefGoogle Scholar
  9. 9.
    Büyüközkan, G., Çifçi, G., Güleryüz, S.: Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Syst. Appl. 38(8), 9407–9424 (2011)CrossRefGoogle Scholar
  10. 10.
    Chang, D.-Y.: Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95(3), 649–655 (1996)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Cheng, C.-H.: Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. Eur. J. Oper. Res. 96(2), 343–350 (1997)CrossRefMATHGoogle Scholar
  12. 12.
    Chen, C.-T.: Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114(1), 1–9 (2000)CrossRefMATHGoogle Scholar
  13. 13.
    Dagdeviren, M.: A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems. J. Intell. Manuf. 21(4), 451–460 (2010)CrossRefGoogle Scholar
  14. 14.
    Dursun, M., Karsak, E.E.: A fuzzy MCDM approach for personnel selection. Expert Syst. Appl. 37(6), 4324–4330 (2010)CrossRefGoogle Scholar
  15. 15.
    Ertugrul, I., Karakasoglu, N.: Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Syst. Appl. 36(1), 702–715 (2009)CrossRefGoogle Scholar
  16. 16.
    Güngör, Z., Serhadlıoğlu, G., Kesen, S.E.: A fuzzy AHP approach to personnel selection problem. Appl. Soft Comput. 9(2), 641–646 (2009)CrossRefGoogle Scholar
  17. 17.
    Heo, E., Kim, J., Boo, K.-J.: Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renew. Sustain. Energy Rev. 14(8), 2214–2220 (2010)CrossRefGoogle Scholar
  18. 18.
    Hwang, C.L., Lai, Y.J., Liu, T.Y.: A new approach for multiple objective decision making. Comput. Oper. Res. 20, 889–899 (1993)CrossRefMATHGoogle Scholar
  19. 19.
    Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer, New York (1981)CrossRefMATHGoogle Scholar
  20. 20.
    Kahraman, C., Cebeci, U., Ulukan, Z.: Multi-criteria supplier selection using fuzzy AHP. Logist. Inf. Manag. 16(6), 382–394 (2003)CrossRefGoogle Scholar
  21. 21.
    Kaya, T., Kahraman, C.: Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Syst. Appl. 38(6), 6577–6585 (2011)CrossRefGoogle Scholar
  22. 22.
    Kumar, P., Singh, R.K.: A fuzzy AHP and TOPSIS methodology to evaluate 3PL in a supply chain. J. Modell. Manag. 7(3), 287–303 (2012)CrossRefGoogle Scholar
  23. 23.
    Kuo, R.J., Chi, S.C., Kao, S.S.: A decision support system for locating convenience store through fuzzy AHP. Comput. Ind. Eng. 37(1–2), 323–326 (1999)CrossRefGoogle Scholar
  24. 24.
    Kutlu, A.C., Ekmekçioglu, M.: Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst. Appl. 39(1), 61–67 (2012)CrossRefGoogle Scholar
  25. 25.
    Kwong, C.K., Bai, H.: Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IIE Trans. 35(7), 619–626 (2003)CrossRefGoogle Scholar
  26. 26.
    Lee, A.H.I., Chen, W.-C., Chang, C.-J.: A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst. Appl. 34(1), 96–107 (2008)CrossRefGoogle Scholar
  27. 27.
    Lee, S.-H.: Using fuzzy AHP to develop intellectual capital evaluation model for assessing their performance contribution in a university. Expert Syst. Appl. 37(7), 4941–4947 (2010)CrossRefGoogle Scholar
  28. 28.
    Liao, C.-N., Kao, H.-P.: An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Syst. Appl. 38(9), 10803–10811 (2011)CrossRefGoogle Scholar
  29. 29.
    Lin, H.-T.: Personnel selection using analytic network process and fuzzy data envelopment analysis approaches. Comput. Ind. Eng. 59(4), 937–944 (2010)CrossRefGoogle Scholar
  30. 30.
    Lootsma, F.A.: Fuzzy Logic for Planning and Decision Making. Kluwer Academic Publisher, Dordrecht (1997)CrossRefMATHGoogle Scholar
  31. 31.
    Matin, H.Z., Fathi, M.R., Zarchi, M.K., Azizollahi, S.: The application of fuzzy TOPSIS approach to personnel selection for Padir Company, Iran. J. Manag. Res. 3(2), 1–14 (2011)Google Scholar
  32. 32.
    Mon, D.-L., Cheng, C.-H., Lin, J.-C.: Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight. Fuzzy Sets Syst. 62(2), 127–134 (1994)CrossRefGoogle Scholar
  33. 33.
    Nazam, M., Xu, J., Tao, Z., Ahmad, J., Hashim, M.: A fuzzy AHP–TOPSIS framework for the risk assessment of green supply chain implementation in the textile industry. Int. J. Supply Oper. Manag. 2(1), 548–568 (2015)Google Scholar
  34. 34.
    Rouyendegh, B.D., Erkan, T.E.: Selection of academic staff using the fuzzy analytic hierarchy process (FAHP): a pilot study. Tehnički vjesnik 19(4), 923–929 (2012)Google Scholar
  35. 35.
    Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1981)MATHGoogle Scholar
  36. 36.
    Samvedi, A., Jain, V., Chan, F.T.S.: Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS. Int. J. Prod. Res. 51(8), 2433–2442 (2013)CrossRefGoogle Scholar
  37. 37.
    Singh, R., Benyoucef, L.: A fuzzy TOPSIS based approach for e-sourcing. Eng. Appl. Artif. Intell. 24(3), 437–448 (2011)CrossRefGoogle Scholar
  38. 38.
    Sun, C.-C.: A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst. Appl. 37(12), 7745–7754 (2010)CrossRefGoogle Scholar
  39. 39.
    Torlak, G., Sevkli, M., Sanal, M., Zaim, S.: Analyzing business competition by using fuzzy TOPSIS method: an example of Turkish domestic airline industry. Expert Syst. Appl. 38(4), 3396–3406 (2011)CrossRefGoogle Scholar
  40. 40.
    Vatansever, K., Oncel, M.: An implementation of integrated multi-criteria decision making techniques for academic staff recruitment. J. Manag. Mark. Logist. 1(2), 111–126 (2014)Google Scholar
  41. 41.
    Wang, T.-C., Chang, T.-H.: Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Syst. Appl. 33(4), 870–880 (2007)CrossRefGoogle Scholar
  42. 42.
    Wang, Y.-M., Elhag, T.M.: Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Syst. Appl. 31(2), 309–319 (2006)CrossRefGoogle Scholar
  43. 43.
    Yong, D.: Plant location selection based on fuzzy TOPSIS. Int. J. Adv. Manuf. Technol. 28(7), 839–844 (2006)CrossRefGoogle Scholar
  44. 44.
    Yoon, K.: A reconciliation among discrete compromise situations. J. Oper. Res. Soc. 38, 277–286 (1987)CrossRefMATHGoogle Scholar
  45. 45.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefMATHGoogle Scholar
  46. 46.
    Zadeh, L.A.: Fuzzy logic, neural network, and soft computing. Commun. ACM 37(3), 77–84 (1994)CrossRefGoogle Scholar
  47. 47.
    Zhang, S.-F., Liu, S.-Y.: A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection. Expert Syst. Appl. 38(9), 11401–11405 (2011)CrossRefGoogle Scholar

Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial EngineeringKadir Has UniversityIstanbulTurkey

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