A Rough-set Based Approach to Design an Expert System for Personnel Selection

Authors: Ehsan Akhlaghi

Abstract:

Effective employee selection is a critical component of a successful organization. Many important criteria for personnel selection such as decision-making ability, adaptability, ambition, and self-organization are naturally vague and imprecise to evaluate. The rough sets theory (RST) as a new mathematical approach to vagueness and uncertainty is a very well suited tool to deal with qualitative data and various decision problems. This paper provides conceptual, descriptive, and simulation results, concentrating chiefly on human resources and personnel selection factors. The current research derives certain decision rules which are able to facilitate personnel selection and identifies several significant features based on an empirical study conducted in an IT company in Iran.

Keywords: Decision Making, Expert System, PersonnelSelection, Rough Set Theory

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082331

References:

[1] W. C. Borman, M. A. Hanson, J. W. Hedge, "Personnel selection," Annual Review of Psychology, pp. 299-337, 1997.
[2] I. T. Robertson, M. Smith, "Personnel selection," Journal of Occupational and Organizational Psychology", pp.441-472, 2001.
[3] E. E. Karsak, "Personnel selection using a fuzzy MCDM approach based on ideal and anti-ideal solutions," Lecture Notes in Economics and Mathematical Systems, pp. 393-402, 2001.
[4] L. S. Chen, C. H. Cheng, "Selecting IS personnel use fuzzy GDSS based on metric distance method," European Journal of Operational Research, pp. 803-820, 2005.
[5] Z. G├╝ngör, G. Serhadlioglu, S. E. Kesen, "A fuzzy AHP approach to personnel selection problem," Applied Soft Computing, vol 9, pp. 641- 646, 2009.
[6] A. Kelemenis, D. Askounis, "A new TOPSIS-based multi-criteria approach to personnel selection," Expert Systems with Applications, vol 37, pp. 4999-5008, 2010.
[7] C. F. Chien, L. F. Chen, "Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry," Expert Systems with Applications, vol 34(1), pp. 280-290, 2008.
[8] M. S. Mehrabad, M. F. Brojeny, "The development of an expert system for effective selection and appointment of the jobs applicants in human resource management," Computers & Industrial Engineering, vol 53, pp. 306-312, 2007.
[9] R. S. Hooper, T. P. Galvin, R. A. Kilmer, J. Liebowitz, "Use of an expert system in a personnel selection process," Expert Systems with Applications, vol. 14(4), pp. 425-432, 1998.
[10] M. Nussbaum et al, "A decision support system for conflict diagnosis in personnel selection," Information and Management, vol. 36, pp. 55-62, Jan. 1999.
[11] C. F. Chien, L. F. Chen, "Using rough set theory to recruit and retain high potential talents for semiconductor manufacturing," IEEE Transactions on Semiconductor Manufacturing, vol 20, pp. 528-541, 2007.
[12] Z. Zou, T. L. Tseng, H. Sohn, G. Song, R. Gutierrez, "A rough set based approach to distributor selection in supply chain management," Expert Systems with Applications, vol. 38, pp. 106-115, 2011.
[13] L.Y. Zhai, L. P. Khoo, Z. W. Zhong, "Towards a QFD-based expert system: A novel extension to fuzzy QFD methodology using rough set theory," Expert Systems with Applications, vol 37, pp. 8888-8896, 2010.
[14] Z. Pawlak, "Rough sets," International Journal of Computer and Information Sciences, vol 11, pp. 341-356, 1982.
[15] C. C. Yeh, D. J. Chi, M. F. Hsu, "A hybrid approach of DEA, rough set and support vector machines for business failure prediction," Expert Systems with Applications, doi:10.1016/j.eswa.2009.06.088, 2009.
[16] Z. Pawlak, "Rough sets", Dordrecht, The Netherlands: Kluwer Academic Publishers, 1991.
[17] A. Kusiak, "Rough set theory: A data mining tool for semiconductor manufacturing," IEEE Transactions on Electronics Packaging Manufacturing, vol 24, pp. 44-50, 2001.