0000000000417227
AUTHOR
Lourdes Canós
Soft computing-based aggregation methods for human resource management
Abstract We are interested in the personnel selection problem. We have developed a flexible decision support system to help managers in their decision-making functions. This DSS simulates experts’ evaluations using ordered weighted average (OWA) aggregation operators, which assign different weights to different selection criteria. Moreover, we show an aggregation model based on efficiency analysis to put the candidates into an order.
Soft Computing Methods for Personnel Selection Based on the Valuation of Competences
Personnel selection based on candidates' competences is a difficult task due to the imprecise description of the applicants' competences and to the existence of several experts simultaneously evaluating those attributes. In this context, fuzzy sets theory provides suitable tools for the attainment of the maximum possible information from imprecise data. In this work, personnel selection methods are proposed that rely on the definition of an ideal candidate. Aggregated fuzzy valuations of each candidate are obtained taking into account the individual valuations provided by the experts. Then, candidates are ranked based on their similarity with the ideal candidate. Three different scenarios a…
Selección de personal basada en métodos difusos
[ES] Las decisiones de los directivos en cuanto a la selección de personal determinan en gran medida el éxito de la empresa. Una elección adecuada de los empleados proporciona una ventaja comparativa. Proponemos un método borroso para la selección de personal basado en la gestión de competencias y la comparación con la valoración que la empresa considera más adecuada para cada trabajo (el candidato ideal). Nuestro método utiliza la distancia de Hamming y el Matching Level Index. Los algoritmos, implementados con el software Sta¿Designer, nos permite establecer un ranking de candidatos, incluso cuando las competencias del candidato ideal han sido evaluadas tan solo en parte. Nuestro enfoque …