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

Diego Alonso Gastélum-Chavira, Denisse Ballardo-Cárdenas () and Ernesto León-Castro
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Diego Alonso Gastélum-Chavira: Department of Economic and Management Sciences, Universidad Autonoma de Occidente, Culiacán 80120, SI, Mexico
Denisse Ballardo-Cárdenas: Department of Engineering and Systems, Universidad Autonoma de Occidente, Culiacán 80120, SI, Mexico
Ernesto León-Castro: Faculty of Economics and Administrative Sciences, Universidad Catolica de la Santisima Concepcion, Concepcion 4081393, Chile

Mathematics, 2024, vol. 12, issue 14, 1-24

Abstract: 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.

Keywords: personnel selection; fuzzy outranking relation; ELECTRE-III; MOEA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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