An Amalgamated Approach of Fuzzy Logic and Genetic Algorithm forBetter Recruitment Process
DOI:
https://doi.org/10.14738/tnc.33.1080Keywords:
Fuzzy number, Triangular Fuzzy Number, Job Recruitment, Robust Ranking Method, Hungarian method, Linguistic Variable, Genetic Algorithms.Abstract
The recruitment process in any departments or organizationsis usually decided by traditional criteria. In today’s scenario, every organization wants to have best employees for their work. Many organizations are used to have separate departments to solve this purpose. But sometimes the recruitment process gets affected by human perceptions, beliefs, pat experiences, feelings, personal relations etc..So, for making recruitment process more automatic and accurate, various authors have proposed their solutions with Hungarian method. In this paper, authors are proposing a method of recruitment by the use of fuzzy triangular number and genetic algorithm with Hungarian method. After performing the first stage (written test) of recruitment with fuzzy triangular number and Hungarian method, the later stages are accomplished with linguistic variables and final recruitment is performed by the use of genetic algorithm.
References
(1) Kuhn, H.W., “The Hungarian Method for the Assignment Problem”, Naval Research Logistics Quarterly, 2:83-97, 1995.
(2) S. P. Eberhardt, T. Duad, A. Kerns, T. X. Brown, A. P. Thakoor, “Competitive Neural Architecture for Hardware Solution to the Assignment Problem”, Neural Network, 4(4), 431-442, 1991.
(3) D. Avis, L. Devroye, “An Analysis of a Decomposition Heuristic for the Assignment Problem”, Oper. Res.Lett., 3(6), 279-283, 1995.
(4) Lin. Chi-Jen, Wen Ue-pyng, “A Labelling Algorithm for the Fuzzy Assignment Problem”, Fuzzy Sets and Systems, 373-391, 2004.
(5) M. S. Chen, “On a Fuzzy Assignment Problem”, Jamkang Journal 22(1985), 407-411.
(6) Long-Sheng Huang, Li-Pu Zhang, “Solution Method for Fuzzy Assignment Problem with Restriction of Qualification”, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA’06), 2006.
(7) Liu. L., Gao X, “Fuzzy Weighted Equilibrium Multi-Job Assignment Problem and Genetic Algorithm”, Applied Mathematical Modelling 33(2009), 3926-3935.
(8) Ye. X, Xu J, “A Fuzzy Vehicle Routing Assignment Model with Connection network based on Priority based Genetic Algorithm”, World Journal of Modelling and Simulation 4(2008), 257-268.
(9) S. J. Chen, S. M. Chen, “Fuzzy Risk Analysis on the Ranking of Generalized Trapezoidal Fuzzy Numbers”, Appl. Intell. 26(2007), 1-11.
(10) S. M. Chen, J. H. Chen, “Fuzzy Risk Analysis based on the Ranking Generalized Fuzzy Numbers with Different Heights and Different Spreads ”, Expert Syst. Appl. 36(2009), 6833-6842.
(11) S. m. Chen, C. H. Wang, “Fuzzy Risk Analysis based on Ranking Fuzzy Numbers using α-cuts, brief features and Signal/Noise ratios”, Expert Syst. Appl. 36(2009), 5576-5581.
(12) C. B. Chen and C. M. Klein, “A Simple Approach to Ranking a Group of Aggregated Fuzzy Utilities”, IEEE Trans. Syst., Man, Cybern. B, Vol. SMC-27, pp. 26-35, 1997.
(13) F. Choobinesh and H. Li, “An Index for Ordering Fuzzy Numbers”, Fuzzy Sets and Systems, Vol. 54, pp. 287-294, 1993.
(14) P. fortemps and M. Roubens, “Ranking and De-fuzzification Methods based Area Compensation”, Fuzzy sets and Systems, Vol. 82, pp. 319-330, 1996.
(15) S. H. Chen, “Ranking Fuzzy Numbers with Maximizing Set and Minimizing Set”, Fuzzy Sets and Systems,17(1985), 113-129.
(16) R. Sivaraj and Dr. T. Ravichandran, “A Review of Selection Methods in Genetic Algorithm”, International Journal of Engineering science and Technology, Vol.3 ,pp.3792-3797,2011.
(17) D.E.Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addition Wesley,2003.
(18) M. Zaki Ramadan, “Effective Staff Selection Tool: Fuzzy Numbers and Memetic Algorithm Based Approach ”, International Jour
