Personnel Selection in a Coffee Shop Company Based on a Multi-Criteria Decision-Aiding and Artificial Intelligence Approach

Human capital management is a strategic element for companies in a globalized world. Therefore, they must use strategies and methods to recruit and select personnel assertively to focus their training, strengthening, and business growth efforts. Personnel selection can be seen as a decision problem...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-07, Vol.12 (14), p.2196
Main Authors: Gastélum-Chavira, Diego Alonso, Ballardo-Cárdenas, Denisse, León-Castro, Ernesto
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Brand loyalty
Coffee
Coffee industry
Decision making
ELECTRE-III
Evolutionary algorithms
Fuzzy logic
fuzzy outranking relation
Fuzzy sets
Genetic algorithms
Heuristic
Human capital
Integer programming
Linear programming
Mathematical programming
MOEA
Multiple criterion
Operations research
Optimization
Personnel selection
Preferences
Product development
Restaurants
Utility functions
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Summary:Human capital management is a strategic element for companies in a globalized world. Therefore, they must use strategies and methods to recruit and select personnel assertively to focus their training, strengthening, and business growth efforts. Personnel selection can be seen as a decision problem and can be addressed in a multi-criteria decision-making context. This work aims to present the selection process of a barista in a Mexican coffee shop. The baristas could be the face of the company to customers, and they could significantly impact their overall experience. The personnel selection process included eleven candidates and three criteria. This process was performed using the ELECTRE-III to model the preferences of a decision-maker and RP2-NSGA-II+H, a multi-objective evolutionary algorithm that exploits fuzzy outranking relations to derive multi-criteria rankings. The ordering obtained with the algorithm did not have any inconsistency concerning the integral preference model, and it allowed for the selection of a candidate to occupy the barista position. The results show the relevance of combining preference modeling with multi-criteria analysis methods for decision-making and artificial intelligence techniques.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12142196