PERSONNEL SELECTION WITH ARAS-G

Year 2017, Volume: 8 , 73 - 79, 10.12.2017

Halil Sen

Abstract

Due to the increasing competition, selection of the most appropriate
personnel is one of the key factors for an organization’s success. The
importance and complexity of the personnel selection problem call for the
methods combining both subjective and objective assessments rather than just
subjective decisions. This paper considers the personnel selection for a new IT
consultant by using an additive ratio assessment method with gray values
(ARAS-G). Analysis of the candidates by ARAS-G method allows determining value
of candidate' in compared with the optimal candidate. As a result, the closest
candidate to the optimal candidate was selected using this method.

Keywords

Personnel selection, multi criteria decision making, additive ratio assessment method, grey values

References

• Ayub, M., Kabir, M. J., & Alam, M. G. R. (2009, December). Personnel selection method using Analytic network Process (ANP) and fuzzy concept. In Computers and Information Technology, 2009. ICCIT'09. 12th International Conference on (pp. 373-378). IEEE. Canós, L., & Liern, V. (2008). Soft computing-based aggregation methods for human resource management. European Journal of Operational Research, 189(3), 669-681. Chen, L. S., & Cheng, C. H. (2005). Selecting IS personnel use fuzzy GDSS based on metric distance method. European journal of operational research, 160(3), 803-820. Dursun, M., & Karsak, E. E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with applications, 37(6), 4324-4330. Gargano, M. L., Marose, R. A., & von Kleeck, L. (1991, October). An application of artificial neural networks and genetic algorithms to personnel selection in the financial industry. In Artificial Intelligence Applications on Wall Street, 1991. Proceedings., First International Conference on (pp. 257-262). IEEE. Gibney, R., & Shang, J. (2007). Decision making in academia: A case of the dean selection process. Mathematical and Computer Modelling, 46(7), 1030-1040. Jessop, A. (2004). Minimally biased weight determination in personnel selection. European Journal of Operational Research, 153(2), 433-444. Karsak, E. E. (2000). A fuzzy multiple objective programming approach for personnel selection. In Systems, Man, and Cybernetics, 2000 IEEE International Conference on (Vol. 3, pp. 2007-2012). IEEE. Karsak, E. E. (2001). Personnel selection using a fuzzy MCDM approach based on ideal and anti-ideal solutions. In Multiple criteria decision making in the new millennium (pp. 393-402). Springer, Berlin, Heidelberg. Kelemenis, A., & Askounis, D. (2010). A new TOPSIS-based multi-criteria approach to personnel selection. Expert systems with applications, 37(7), 4999-5008. Liu, S., & Forrest, J. Y. L. (2010). Grey systems: theory and applications. Springer. Petrovic‐Lazarevic, S. (2001). Personnel selection fuzzy model. International Transactions in Operational Research, 8(1), 89-105. Tupenaite, L., Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Seniut, M. (2010). Multiple criteria assessment of alternatives for built and human environment renovation. Journal of Civil Engineering and Management, 16(2), 257-266. Turskis, Z., & Zavadskas, E. K. (2010). A novel method for multiple criteria analysis: grey additive ratio assessment (ARAS-G) method. Informatica, 21(4), 597-610. Wang, D. (2009, September). Extension of TOPSIS method for R&D personnel selection problem with interval grey number. In Management and Service Science, 2009. MASS'09. International Conference on (pp. 1-4). IEEE. Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision making. Technological and Economic Development of Economy, 16(2), 159-172. Zavadskas, E. K., Turskis, Z., & Vilutiene, T. (2010). Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method. Archives of civil and mechanical engineering, 10(3), 123-141.