An AI for Robust MCDM Ranking in a Large Number of Criteria

Tanya S. Garcia-Gastelum, Cristhian R. Uzeta-Obregon, Pavel Álvarez-Carrillo () and Ernesto León-Castro ()
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Tanya S. Garcia-Gastelum: Unidad Regional Culiacan, Universidad Autonoma de Sinaloa, Culiacan 80058, Sinaloa, Mexico
Cristhian R. Uzeta-Obregon: Department of Economic and Management Sciences, Universidad Autonoma de Occidente, Culiacan 80020, Sinaloa, Mexico
Pavel Álvarez-Carrillo: Department of Economic and Management Sciences, Universidad Autonoma de Occidente, Culiacan 80020, Sinaloa, Mexico
Ernesto León-Castro: Faculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción, Concepcion 4081393, Chile

Mathematics, 2025, vol. 13, issue 17, 1-20

Abstract: An artificial intelligence procedure (AIP) model is presented to generate a robust multicriteria ranking for decision-making in problems with a large number of criteria, which jointly uses genetic algorithms, the OWA method, and SMAA. The main contribution of this AIP model is to reduce the cognitive effort of the decision-maker in determining the weights of the criteria involved in the decision problem, since, with the proposed model, these weights are inferred according to the decision-maker’s preferences, which can result in a ranking of the alternatives called a robust ranking. Robustness provides the expert with the certainty that, despite variations in their preferences and, therefore, in the weights inferred by the model, the presented robust ranking will not undergo significant changes, giving stability to the decision-making solution in a stable environment and reflecting the decision-maker’s preferences. This is one of the main contributions of applying the AIP model, unlike other models that do not provide a robust solution, as described in the application of the proposed AIP model to a multicriteria decision problem with many criteria, such as the state competitiveness analysis problem presented by the Mexican Institute for Competitiveness (IMCO).

Keywords: artificial intelligence; robust ranking; SMAA; IOWA; MCDM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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