Decisão multicritério orientada por ênfases do decisor: uma abordagem estatística para decisões alinhadas a diretrizes organizacionais

Abstract

Several decision-making problems involve the analysis of multiple criteria that are often conflicting with one another. In this context, the resulting scenarios are complex, which calls for systematic methods to find solutions that adequately address all the criteria involved. The socalled multicriteria decision support methods can be used to assist in solving this type of problem. Among the various applicable methods, the AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) stand out as the most widely used in the literature, along with numerous studies that propose variations and enhancements of these methods to expand their applicability across different decision contexts. This study proposes three new methodological approaches for multicriteria decision-making focused on emphasizing preferable criteria, namely the AHP-Equitative, AHP-MEDAC, and Hierarchical TOPSIS-MEDAC methods. These approaches aim to increase the weights of preferable criteria through the application of statistical measures to the performance of the alternatives in each criteria, specifically skewness and variability are used. The AHP-Equitative, inspired by the Gaussian AHP method, was the first approach proposed in this research, and some of its statistical limitations inherited from the Gaussian AHP are addressed in the AHP-MEDAC method. To validate the proposed methods (AHP-Equitative, AHP-MEDAC and Hierarchical TOPSISMEDAC), they were applied within the educational context, resulting in a ranking constructed based on emphases previously defined by the decision-maker (promotion of equity), maintaining coherence and alignment with the institutional guidelines, policies, and strategic objectives that underpin any multicriteria decision process. The results were satisfactory, demonstrating an increase in the weights of the emphasized criteria and greater contrasts among the scores of the alternatives, which tend to mitigate the level of criticality in decision-making. Moreover, the methods demonstrated robustness across different modeling scenarios.