Weighted Fuzzy Group Decision-making in Recruitment

Autores/as

  • Michal Škoda Czech University of Life Sciences Prague
  • Helena Brožová Czech University of Life Sciences Prague

Palabras clave:

α-level, Decision making, fuzzy number, Hamming distance, linguistic scale, Recruitment

Resumen

This article deals with group decision making during recruitment of new employees. The importance of correct selection of a candidate during the recruitment is very easy to understand, but not so easy to achieve. Especially in situations where decision-makers have to evaluate large number of different candidates. The proposed approach was created to help decision-makers to find the most suitable candidate by using only vague expressions. Given the uncertainty, subjectivity and ambiguity of human knowledge, the entire approach is based on Fuzzy Set Theory. More specifically on new innovative transformation of fuzzy numbers through α-level cuts.

The transformation of fuzzy numbers will be used to taking into account weigh of each member of the decision-making group and the weights will be calculated by using the AHP method. The shape and position of fuzzy numbers play the key role in the transformation. Additionally, the Hamming distance will be used for the final interpretation of the results, thus there is no loss of information caused by defuzzification.

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Citas

Abdullah, L. and Norsyahida Z. (2015) ‘Integration of Fuzzy AHP and Interval Type-2 Fuzzy DEMATEL: An

Application to Human Resource Management’, Expert Systems with Applications, vol. 42, no. 9, pp. 4397–4409. https://dx.doi.org/10.1016/j.eswa.2015.01.021

Baležentis, A., Baležentis , T. and Brauers, W.K.M. (2012) ‘Personnel Selection Based on Computing

with Words and Fuzzy MULTIMOORA’, Expert Systems with Applications, vol. 39, no. 9, pp. 7961–7967. https://dx.doi.org/10.1016/j.eswa.2012.01.100

Breaugh, J.A. (2009) ‘Employee Selection at the Beginning of the 21st Century’, Human Resource Management Review, vol. 19, no. 3, pp. 167–168. https://dx.doi.org/10.1016/j.hrmr.2009.03.009

Butenko, S. and Pardalos, P.M. (2017) Optimization Methods and Applications: in Honor of Ivan V. Sergienkos 80th Birthday. Springer, 2017.

Chen, L., Huang, Y. and Chen, R.B. (2017) ‘Regional Disaster Risk Evaluation of China Based on the Universal Risk Model’, Natural Hazards, vol. 89, no. 2, pp. 647–660. https://dx.doi.org/10.1007/s11069-017-2984-2

Cho, H., Park, G., Ryu, J. and Park, H. (2019) ‘Decision-Making Process for Places of Refuge in Hazardous and Noxious Substances Incident: Case Study of South Korea’, Marine Policy, vol. 108, pp. 103643. https://dx.doi.org/10.1016/j.marpol.2019.103643

Golec, A. and Kahya, E. (2007) ‘A Fuzzy Model for Competency-Based Employee Evaluation and Selection’, Computers & Industrial Engineering, vol. 52, no. 1, pp. 143–161. https://dx.doi.org/10.1016/j.cie.2006.11.004

Brozova, H., Subrt, T. and Vorlickova, L. (2009) ‘The System Approach to Knowledge Creation, Sharing and Utilisation in Managerial Competency Models’, International Journal of Learning and Intellectual

Capital, vol. 6, no. 1/2, pp. 103. https://dx.doi.org/10.1504/ijlic.2009.021722

Huang, Y. (2012) ‘Abrupt disaster events oriented emergency response effectiveness evaluation method’, Journal of Natural Disasters, vol. 21, no. 1, pp. 71–77.

Shallit, J. (2009) ‘Hamming distances for conjugates’, Discrete Mathematics, vol. 309, no. 12, pp. 4197-4189. https://dx.doi.org/10.1016/j.disc.2008.11.001

Ertugrul Karsak, E. (2001) ‘Personnel Selection Using a Fuzzy MCDM Approach Based on Ideal and Anti-Ideal Solutions’, Multiple Criteria Decisión Making in the New Millennium. Lecture Notes in Economics and Mathematical Systems, vol. 507, pp. 393–402. https://dx.doi.org/10.1007/978-3-642-56680-6_36

Klir, G.J. and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic: Theory and Applications. Pearson.

Kumar, S. and Joshi, D. (2018) ‘Fuzzy Ideal Based Computational Approach for Group Decision Making Problems’, Fuzzy Information and Engineering, vol. 9, no. 2, pp. 247–258. https://dx.doi.org/10.1016/j.fiae.2017.06.008

Lin, C.-J. and Wu, W.-W. (2008) ‘A Causal Analytical Method for Group Decision-Making under Fuzzy Environment’. Expert Systems with Applications, vol. 34, no. 1, pp. 205–213. https://dx.doi.org/10.1016/j.eswa.2006.08.012

Mammadova, M.H. Jabrayilova, Z.Q. and Mammadzada, F.R. (2016) ‘Fuzzy Multi-Scenario Approach to Decision-Making Support in Human Resource Management’, Recent Developments and New Direction in Soft-Computing Foundations and Applications Studies in Fuzziness and Soft Computing, vol. 342, pp. 19–36. https://dx.doi.org/10.1007/978-3-319-32229-2_3

Medasani, S., Kim, J. and Krishnapuram, R. (1998). ‘An Overview of Membership Function Generation Techniques for Pattern Recognition’, International Journal of Approximate Reasoning, vol. 19, no. 3-4, pp. 391–417. https://dx.doi.org/10.1016/s0888-613x(98)10017-8

Mianabadi H. and Afshar, A. (2008) ‘A new method to evaluate weights of decision makers and its application in water resource management’, Proceedings of the 13th IWRA World Water Congress, pp. 1–10.

Nahmias, S. (1978) ‘Fuzzy variables’, Fuzzy Sets and Systems, vol 1, no. 2, pp. 97–110. https://dx.doi.org/10.1016/0165-0114(78)90011-8

Pamučar, D., Stević, Ž. and Sremac, S. (2018) ‘A New Model for Determining Weight Coefficients of

Criteria in MCDM Models: Full Consistency Method 15 (FUCOM)’, Symmetry, vol. 10, no. 9, pp. 393. https://dx.doi.org/10.3390/sym10090393

Ren, P., Xu, Z. and Liao, H. (2016) ‘Intuitionistic Multiplicative Analytic Hierarchy Process in Group Decision Making’, Computers & Industrial Engineering, vol. 101, pp. 513–524. https://dx.doi.org/10.1016/j.cie.2016.09.025

Saaty, T.L. (1977) ‘A Scaling Method for Priorities in Hierarchical Structures’, Journal of Mathematical Psychology, vol. 15, no. 3, pp. 234–281. https://dx.doi.org/10.1016/0022-2496(77)90033-5

Saaty, T.L. (1980) The Analytic Hierarchy Process: Planning. McGraw-Hill, New York.

Saaty, R.W. (1987) ‘The Analytic Hierarchy Process - What It Is and How It Is Used’, Mathematical Modelling, vol. 9, no. 3-5, pp. 161–176. https://dx.doi.org/10.1016/0270-0255(87)90473-8

Saaty, T.L. (2006) Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process. RWS Publications.

Safarzadeh, S., Khansefid, S. and Rasti-Barkozi, M. (2018) ‘A Group Multi-Criteria Decision-Making Based on Best-Worst Method’, Computers & Industrial Engineering, vol. 126, pp. 111–121. https://dx.doi.org/10.1016/j.cie.2018.09.011

Shahhosseini, V. and Sebt, M.H. (2011) “Competency-Based Selection and Assignment of Human Resources to Construction Projects.” Scientia Iranica, vol. 18, no. 2, pp. 163–180. https://dx.doi.org/10.1016/j.scient.2011.03.026

Slaughter, J.E., Bagger, J. and Li, A. (2006) ‘Context Effects on Group-Based Employee Selection Decisions’, Organizational Behavior and Human Decision Processes, vol. 100, no. 1, pp. 47–59. https://dx.doi.org/10.1016/j.obhdp.2006.01.003

Yu, C.-S. (2002) ‘A GP-AHP Method for Solving Group Decision-Making Fuzzy AHP Problems’, Computers & Operations Research, vol. 29, no. 14, pp. 1969–2001. https://dx.doi.org/10.1016/s0305-0548(01)00068-5

Zadeh, L.A. (1964) Fuzzy Sets, University of Berkely, California. https://dx.doi.org/10.21236/ad0608981

Zhang R., Phillis, Y. and Kouikoglou, V. (2005) Fuzzy Control of Queuing Systems. Springer-Verlag London, pp 15-25.

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Publicado

2020-02-04

Cómo citar

Škoda, M., & Brožová, H. (2020). Weighted Fuzzy Group Decision-making in Recruitment. Revista Latinoamericana De Investigación Social, 2(3), 1–15. Recuperado a partir de https://revistasinvestigacion.lasalle.mx/index.php/relais/article/view/2246

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