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A linguistic decision model for personnel management solved with a linguistic biobjective genetic algorithm

Authors: Herrera, F.; López-González, E.; Mendaña-Cuervo, C.; Rodríguez-Fernández, M. A.;

A linguistic decision model for personnel management solved with a linguistic biobjective genetic algorithm

Abstract

Abstract Staff selection for the varying activities performed by enterprises requires a coherent approach, which cannot be simplistic, to the information held. The use of flexible computation and the vague representation of knowledge available by means of linguistic labels allow the problem to be recognised as it is in real life. This paper is an attempt to supply a satisfactory solution to a real staff management problem with linguistic information presenting a linguistic decision model for personnel problem. For reaching a good solution, a novelty genetic algorithm with a linguistic biobjective fitness function is proposed.

Keywords

Linguistic labels, Staff selection, Genetic algorithms, Relationships between jobs, Linguistic operators

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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