A Rough-set Based Approach to Design an Expert System for Personnel Selection
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.
[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.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:63093", author = "Ehsan Akhlaghi", title = "A Rough-set Based Approach to Design an Expert System for Personnel Selection", 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", volume = "5", number = "6", pages = "1148-4", }