A group MADM method for personnel selection problem using Delphi technique based on intuitionistic fuzzy sets

Abstract

Personnel selection (PS) is an important problem for an organization while the competition in global markets increases. PS, is a decision making process consisting of vagueness and imprecision. In real world, decision makers’ experience, position through the organization, effectiveness in the group and field of expertise for each attribute in the group influence decision making process for PS. In this study, a group multi attribute decision making method has been developed using Delphi Technique based on intuitionistic fuzzy sets in sensitivity of experts to exploit the uncertainty and to take account of decision makers’ importance for each attribute in the PS problem. The proposed method was applied in a case study. Case study showed that taking into account weights of decision makers for each attribute affect the result of the process of personnel selection.

Keywords

• Delphi technique
• group decision making
• intuitionistic fuzzy sets
• multi attribute decision making
• personnel selection problem

Kaynakça

1. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87–96.
2. Atanassov, K. T. (1999). Intuitionistic fuzzy sets. Heidelberg: Springer.
3. Atanassov, K., Pasi, G. & Yager, R.R. (2005).
4. Intuitionistic fuzzy interpretations of multi-criteria multiperson and multi-measurement tool decision making. International Journal of Systems Science, 36 (14), 859– 8 Boran, F.E., Genç, S. Genç, Kurt, M. & Akay, D. (2009).
5. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36, 11363–11368.
6. Chang, C., Wu, C., Lin, C. & Chen, H. (2007). An application of AHP and sensitivity analysis for selecting the best slicing machine. Computers & Industrial Engineering, 52, 296–307.
7. Chen, C.T. (2000). Extension of the TOPSIS for group decision making under fuzzy environments, Fuzzy Sets and System. 114, 1–9.
8. Chen, S.M. & Tan, J. M. (1994). Handling multi criteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems, 67, 163–172.

9. Chen, L.S. & Cheng, C.H. (2005). Selecting IS personnel use fuzzy GDSS based on metric distance method.
10. European Journal of Operation Research, 160, 803–820. Chien, C. & Chen, L. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34, 280–290.
11. Dağdeviren, M.. (2010). A hybrid multi-criteria decisionmaking model for personnel selection in manufacturing systems. Journal of Intelligent Manufacturing, 21(4), 4514
12. Dalkey, N. C. & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts.
13. Management Science, 9, 458–467. Dursun, M. & Karsak, E.E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37, 4324–4330.
14. Gerdsri, N. & Kocaoglu, D.F. (2007). Applying the Analytic Hierarchy Process (AHP) to build a strategic framework for technology roadmapping. Mathematical and Computer Modelling, 46, 1071–1080.
15. Göleç, A. & Kahya, E. (2007). A fuzzy model for competency-based employee evaluation and selection.
16. Computers & Industrial Engineering, 52, 143–161. Güngör, Z., Serhadlıoğlu, G. & Kesen, S.E. (2009). A fuzzy AHP approach to personnel selection problem.
17. Applied Soft Computing, 9, 641–646. Handfield, R., Walton, S.V., Sroufe, R. & Melnyk, S.A. (2002). Applying environmental criteria to supplier assessment: a study in the application of the Analytical
18. Hierarchy Process. European Journal of Operational Research, 141, 70–87. Hong, D.H. & Choi, C.H. (2000). Multi criteria fuzzy decision-making problems based on vague set theory.
19. Fuzzy Sets and Systems, 114, 103–113. Huang, D.K., Chiu, H.N., Yeh, R.H. & Chang, J.H. (2009). A fuzzy multi-criteria decision making approach for solving a bi-objective personnel assignment problem.
20. Computers & Industrial Engineering, 56, 1–10. Jahanshahlo, G.R., Hosseinzade, L.F. & Izadikhah, M. (2006a). An algorithmic method to extend TOPSIS for decision making problems with interval data. Applied
21. Mathematics and Computation, 175, 1375–1384.
22. Jahanshahlo, G.R., Hosseinzade, L. F. & Izadikhah, M. (2006b). Extension of the TOPSIS method for decision making problems with fuzzy data. Applied Mathematics and Computation, 81, 1544–1551.
23. Jessop, A. (2004). Minimally biased weight determination in personnel selection. European Journal of Operation Research, 153, 433–444.
24. Karsak, E.E. (2001). Personnel selection using a fuzzy
25. MCDM approach based on ideal and anti-ideal solutions. Lecture Notes in Economics and Mathematical Systems, 507, 393–402. Kelemenis, A. & Askounis, D. (2010). A new TOPSISbased multi-criteria approach to personnel selection.
26. Expert Systems with Applications, 37, 4999–5008.
27. Lazarevic, S.P. (2001). Personnel selection fuzzy model, International Transactions in Operational Research. 8, 89–
28. Li, D-F. (2007). A fuzzy proximity approach to fuzzy multi-attribute decision making. Fuzzy Optimization and Decision Making, 6 (3) 237-254.
29. Liang, G-S. & Wang, M-J.J. (1994). Personnel selection using fuzzy MCDM algorithm. European Journal of
30. Operational Research, 78 (1) 22-33. Lin, H.-T. (2010). Personnel selection using analytic network process and fuzzy data envelopment analysis approaches. Computers & Industrial Engineering, 59, 937– 9
31. Liu, H.W. & Wang, W.G. (2007). Multi-criteria decisionmaking methods based on intuitionistic fuzzy sets.
32. European Journal of Operational Research, 179, 220–233. Liu, L., Huan, Y.X. and Xia, Z.Q. (2007). Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy sets. Journal of Computer and System Sciences, 73, 84–
33. Malinowski, J., Weitzel, T. & Keim, T. (2008). Decision support for team staffing: An automated relational recommendation approach. Decision Support Systems, 45, 429–447.
34. Shen, Y.-C., Chang, S.-H., Lin, G.T.R. & Yu, H.-C. (2010). A hybrid selection model for emerging technology.
35. Technological Forecasting & Social Change, 77, 151–166. Szmidt, E. & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114, 505–518.
36. Szmidt, E. & Kacprzyk, J. (2002). Using intuitionistic fuzzy sets in group decision making. Control and Cybernetics, 31, 1037–1053.
37. Szmidt E. & Kacprzyk, J. (2003). A consensus-reaching process under intuitionistic fuzzy preference relations.
38. International Journal of Intelligent Systems, 18, 837–852. Tavana, M., Pirdashti, M., Kennedy, D.T., Belaud, J.-P. & Behzadian, M. (2012). A hybrid Delphi-SWOT paradigm for oil and gas pipeline strategic planning in Caspian Sea basin. Energy Policy, 40, 345–360.
39. Triantaphyllou, E. & Lin, C.T. (1996). Development and evaluation of five fuzzy multi attribute decision-making methods. International Journal of Approximate Reasoning, 14, 281–310.
40. Xu, Z.S. (2007a). Intuitionistic preference relations and their application in group decision making. Information Sciences, 177, 2363–2379.
41. Xu, Z.S. (2007b). Models for multiple attribute decision making with intuitionistic fuzzy information. International
42. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15, 285–297. Xu, Z.S. (2007c). Intuitionistic fuzzy aggregation operators. IEE Transaction of Fuzzy Systems, 15 (6), 1179–1187.
43. Xu, Z. & Cai, X. (2010). Recent advances in intuitionistic fuzzy information aggregation. Fuzzy Optimization and Decision Making, 9 (4), 359-381.
44. Wang, P. (2009). QoS-aware web services selection with intuitionistic fuzzy set under consumer’s vague perception.
45. Expert Systems with Applications, 36 (3), 4460–4466.
46. Zhang, S. & Liu, S. (2011). A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection. Expert Systems with Applications, 38, 11401–11405.