Application of Fuzzy Optimization Method in Decision-Making for Personnel Selection


The paper describes the necessity of application of intelligent technologies to support decisions of more objective problems in human resource management. In this paper, we describe the methodology for personnel selection problem for the vacancy with regard to the importance and nonequivalence of numerous indicators characterizing the alternatives. The specific features of the selection problem are highlighted, immersing the problem into a fuzzy environment. A fuzzy multicriterial model of the personnel selection problem is proposed. A technique of order preference by similarity to ideal solition (TOPSIS), was applied for evaluation and regulation of alternatives. This technique is based on criteria of qualitative character, which are hierarchically structured by multiple experts to intellectually support decisions made in personnel selection problem. Using TOPSIS method and generated criteria system an experiment was conducted for evaluation of the candidates during solution of hiring problems. The obtained and reviewed results were compared with results obtained using in reality.

Share and Cite:

Mammadova, M. and Jabrayilova, Z. (2014) Application of Fuzzy Optimization Method in Decision-Making for Personnel Selection. Intelligent Control and Automation, 5, 190-204. doi: 10.4236/ica.2014.54021.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Cole, G.A. (2002) Personnel and Human Resource Management. 5th Edition, Thomson Learning, Wadsworth, 448 p.
[2] Spencer, M.L. and Spencer, S.M. (1993) Competence at Work: Models for Superior Performance. John Wiley & Sons, Inc., New York, 384 p.
[3] Bazarov, T.Y. (2009) Personnel Management. UNITY-DANA Publ., Moscow, 240 p.
[4] Makarova, I.K. (2007) Human Resource Management. DELO Publ., Moscow, 232 p.
[5] Ivantsevich, J.M. and Lobanov, A.A. (2004) Human Resources Management. Gordarike publ., Moscow, 245 p.
[6] Trachtengertz, E.A. (2001) Capabilities and Realization of Computer Decision Making Support Systems. News of Academy of Sciences of Russia. Management Theory and Systems, 3, 86-113.
[7] Larichev, O.I. (2000) Theory and Methods of Decision Making, Logos, Moscow, 296 p.
[8] Mikoni, S.V. (2009) Multicriteria Selection on the Final Alternative Set. Student Handbook, LAN Publ., Saint Petersburg, 270 p.
[9] Mammadova, M.G. and Jabrayilova, Z.Q. (2013) Application of TOPSIS Method in Support of Decisions Made in Staff Management Issues. Computer Technology and Application, 4, 307-316.
[10] Management of Organization Personnel (2005) Manual. Under Edition of Kibanov, Infra-M Publ., Moscow, 638 p.
[11] Nussbaum, M., Singer, M., Rosas, R., Castillo, M., Flies, E., Lara, R. and Sommers, R. (1999) Decision Support System for Conflict Diagnosis in Personnel Selection. Information & Management, 36, 55-62.
[12] Storey Hooper, R., Galvin, T.P., Kilmer, R.A. and Liebowitz, J. (1998) Use of an Expert System in a Personnel Selection Process. Expert Systems with Applications, 14, 425-432.
[13] Akhlagh, E. (2011) A Rough-Set Based Approach to Design an Expert System for Personnel Selection. World Academy of Science, Engineering and Technology, 54, 202-205.
[14] Borman, W.C., Hanson, M.A. and Hedge, J.W. (1997) Personnel Selection. Annual Review of Psychology, 48, 299-337.
[15] Robertson, T. and Smith, M. (2001) Personnel Selection. Journal of Occupational and Organizational Psychology, 74, 441-472.
[16] Dursun, M. and Karsak, E. (2010) A Fuzzy MCDM Approach for Personnel Selection. Expert Systems with Applications, 37, 4324-4330.
[17] Chen, L.S. and Cheng, C.H. (2005) Selecting IS Personnel Use Fuzzy GDSS Based on Metric Distance Method. European Journal of Operational Research, 160, 803-820.
[18] Gungor, Z., Serhadl?oglu, G. and Kesen, S.E. (2009) A Fuzzy AHP Approach to Personnel Selection Problem. Applied Soft Computing, 9, 641-649.
[19] Wang, Y.J. and Lee, H.S. (2007) Generalizing TOPSIS for Fuzzy Multiple-Criteria Group Decision-Making. Computers and Mathematics with Applications, 53, 1762-1772.
[20] Kelemenis, A. and Askounis, D. (2010) A New TOPSIS-Based Multi-Criteria Approach to Personnel Selections. Expert Systems with Applications, 37, 4999-5008.
[21] Nobari, S. (2011) Design of Fuzzy Decision Support System in Employee Recruitment. Journal of Basic and Applied Scientific Research, 1, 1891-1903.
[22] Mammadova, M.H., Jabrayilova, Z.G. and Nobari, S.M. (2012) Application of TOPSIS Method in Support of Decisions Made in Staff Management Issues. IV International Conference “Problems of Cybernetics and Imformaics” (PCI-2012), Vol. IV, 12-14 September 2012, Baku, 195-198.
[23] Chien, C.F. and Chen, L.F. (2008) Data Mining to Improve Personnel Selection and Enhance Human Capital: A Case Study in High-Technology Industry. Expert System with Applications, 34, 280-290.
[24] Chen, P.C. (2009) A Fuzzy Multiple Criteria Decision Making Model in Employee Recruitment. IJCSNS International Journal of Computer Science and Network Security, 9, 113-117.
[25] Mehrabad, M.S. and Brojeny, M.F. (2007) The Development of on Expert System for Effective Selection and Appointment of the Jobs Applicants in Human Resource Management. Computer and Industrial Engineering, 53, 306-312.
[26] Larichev, O.I. and Sternin, M. (1998) Decision Support System of Multi-Objective Problem of Assignment. Information Systems and Processes, 3, 10-16.
[27] Werner, J.M. (2000) Implications of OCB and Contextual Performance for Human Resource Management. Human Resource Management Review, 10, 3-24.
[28] Chen, C.T., Lin, C.T. and Huang, S.F. (2006) A Fuzzy Approach for Supplier Evaluation and Selection in Supply Chain Management. International Journal of Production Economics, 102, 289-301.
[29] Tai, W.S. and Hsu, C.C. (2006) A Realistic Personnel Selection Tool Based on Fuzzy Data Mining Method. Proceedings of the 9th Joint Conference on Information Sciences (JCIS), Taiwan, 8-11 October 2006. file:///C:/Users/HP/Downloads/JCIS06-FTT-56%20(3).pdf
[30] Saaty, T.L. (1990) How to Make a Decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48, 426-447.
[31] Neumann, J.V. and Morgenstern, O. (2007) Theory of Games and Economic Behavior. One of Princeton University Presses, Notable Centenary Titles, 776 p.
[32] Mammadova, M.H., Jabrayilova, Z.G. and Manafli, M.I. (2009) Monitoring of Demands for Information Technology Specialists. Information Technology Publ., Baku, 199 p.
[33] Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353.
[34] Kofman, A. (1982) Introduction into the Theory of Fuzzy Sets. Radio and connection, Мoscow, 432 p.
[35] Orlovskiy, S.А. (1981) Problems of Decision Making at Fuzzy Initial Information. Nauka, Мoscow, 208 p.
[36] Chen, C.T. (2000) Extensions of the TOPSIS for Group Decision-Making under Fuzzy Environment. Fuzzy Sets and Systems, 114, 1-9.
[37] Jabrailova, Z.G. and Nobari S.M. (2011) Defining Methods of Importance Factor of the Criteria in the Solution of Personnel Management Problems and Detection of Contradictions. Proceedings of the 11th International Conference on Pattern Recognition and Information Processing (PRIP’2011), May 2011, Minsk, 330-333.
[38] Hsu, H.M. and Chen, C.T. (1997) Fuzzy Credibility Relation Method for Multiple Criteria Decision-Making Problems. Information Sciences, 96, 79-91.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.