Personnel Selection in a Competitive Environment

Authors

  • Marilyn Bello Universidad Central de Las Villas
  • Rafael Bello Universidad Central de Las Villas
  • María M. García-Lorenzo Universidad Central de Las Villas
  • Ann Nowe Universiteit Brussel

DOI:

https://doi.org/10.13053/cys-20-2-2315

Keywords:

Personnel selection, decision making, game theory.

Abstract

The personnel selection problem is a classical decision making problem. It refers to the process of choosing candidates who match, possibly to some degree, the qualifications required to perform a certain job. Personnel selection is an important activity for organizations and usually the outcome of a personnel selection method is an overall ranking of the candidates. This paper introduces two new results. First, we propose an alternative approach to the personnel selection problem in which the interaction of two competing decision makers (employers), who must select two subsets of persons from a common list of candidates, is considered. Second, given the rankings of the candidates for each employer, a method based on the game theory is presented to solve this problem. 

Author Biographies

Marilyn Bello, Universidad Central de Las Villas

Marilyn Bello received the B.Sc. degree (with honors) in Computer Sciences from the Las Villas Central University “Marta Abreu” (UCLV), Cuba, in 2012. She is a Professor at the Computer Science Department. She has authored/coauthored several papers in conference proceedings and scientific journals. She obtained several awards in different student scientific events. Her research interests include metaheuristics, machine learning, and decision making.

Rafael Bello, Universidad Central de Las Villas

Rafael Bello received his Bachelor degree in Mathematics and Computer Science (1982) from the Las Villas Central University “Marta Abreu”, Santa Clara, Cuba, and his Ph.D. in Mathematics from UCLV in 1988. He has been a visiting scholar at some universities in Spain, Germany, and Belgium. He is a Full Professor at the Computer Science Department, UCLV, Cuba, and exhibits a long record of academic exchange with many universities in Latin America and Europe. He has taught more than 45 undergraduate and graduate courses in those academic centers. He has authored/edited 9 books, published over 180 papers in conference proceedings and scientific journals, supervised over 30 Bachelor, Master, and Ph.D. theses. He has received prestigious awards from the Cuban Academy of Sciences and other renowned scientific societies. His research interests comprise soft computing (rough and fuzzy set theories), metaheuristics, machine learning, and decision making.

María M. García-Lorenzo, Universidad Central de Las Villas

María M. García-Lorenzo received the B.Sc. degree in Mathematical Cybernetics from the Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba, in 1985 and her Ph.D. in Technical Sciences from UCLV in 1997. She is a Full Professor at the Artificial Intelligence Lab of the Center of Studies on Informatics, UCLV, where she is also the General Coordinator of the Master in Computer Science Program (AUIP awarded). Also, she exhibits a long record of academic exchange with many universities in Latin America and Europe. Dr. Garcia has authored 6 books, published over 150 papers in conference proceedings and scientific journals and supervised several Bachelor, Master, and Ph.D. theses. She has received numerous prestigious awards from the Cuban Academy of Sciences and other renowned scientific societies. Her research interests include neural networks, soft computing, machine learning, and pattern recognition.

Ann Nowe, Universiteit Brussel

Ann Nowé received the M.S. degree from Universiteit Gent, Belgium, in 1987, where she studied mathematics with a minor in computer science, and the Ph. D. degree from Vrije Universiteit Brussels (VUB), Belgium, in collaboration with Queen Mary and Westfield College, University of London, U.K., in 1994. Currently she is a Full Professor at the VUB and co-head of the AI/COMO Lab VUB. Her major area of interest is machine learning, especially reinforcement learning in single- as well as multi-agent settings. The developed approaches have been evaluated in a variety of applications such as telecommunication, smart grids, and mechatronics.

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Published

2016-06-25

Issue

Vol. 20 No. 2 (2016): 20-2 (2016)

Section

Articles

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