## 1 Introduction  
Complex environments, conflicting criteria, uncertainties, and inaccurate information are characteristic of many decision problems in the real world. Efficiency in decision-making consists of choosing the alternative that, as far as possible, offers the best results. The viable alternatives for achieving the objective, and selected for evaluation, are compared according to criteria and under the influence of several attributes [1]. In this sense, the Multicriteria Decision Making (MCDM) methodology contributes to making the decision-making process more rational [2, 3]. The MCDM methods support the decision-making process because they consider value judgments and not only technical issues to evaluate alternatives in real problems, presenting high multidisciplinary [4]. The MCDM methodology comprises multiple criteria to help stakeholders and groups explore relevant decisions [5]. The essential components of MCDM are a set of alternatives,criteria, and at least one decision-maker (DM). MCDM assists in decision-making, especially concerning problems of choosing, ordering, or classifying actions [6]. Another vital feature is that multicriteria methods are not designed to seek the best alternative concerning all criteria. The problem's difficulty originates from more than one criterion [7]. The problems of choice imply the selection of a subset containing the best alternatives; classification problems provide the alternatives, from the best to the worst; classification problems distribute the alternatives into predefined and ordered categories [8]. We emphasize the relevance of the hierarchy between data, information, and knowledge when applying the methods. The data, when processed and analyzed, become information. When such information is recognized and applied in decision-making, it generates knowledge. Similarly, the reverse hierarchy can also be applied since knowledge, when disseminated or explained, becomes information that, when dismembered, can generate a data set [9]. Figure 1 presents this logic, which is fundamental to MCDM. The people represent one of the most significant resources of an organization. Therefore, personnel selection is one of the problems that organizations are increasingly facing [10]. According to [11], staff selection is a significant task for any organization that aims to select the most suitable candidates to fill well-defined vacancies. In the recruitment process, several characteristics are involved, such as leadership, analytical skills, independent thinking, innovation, endurance, and personality. In this context, a vital division in any organization is the Human Resources (HR) department, responsible for personnel recruitment and selection [12]. In the knowledge economy, human capital is a crucial factor in any organization achieving sustainable competitive advantage. Thus, personnel selection is the most important function of HR managers [13]. However, the numerous criteria, alternatives, and goals make choosing between several candidates excessively complex and confusing. Inaccuracy and uncertainty characterize the decision-making environment, making it difficult to express the exact criteria [10]. Multicriteria techniques can help to address personnel assessment problems, which include several candidates as alternatives, along with several complex evaluation criteria [12; 13; 14; 15]. As suggested by [16], decision-aid methods should enable an integrated algorithm to evaluate qualitative and quantitative data. That is, tools should be able to structure and analyze variables in situations where the DM cannot define a precise numeric entry. In this context, subjectivity in MCDM modeling plays an important role [17]. It transcribes the preferences of the DM by the method implemented, being transparent regarding the manipulations and attributions in the evaluation problem [18].