ABSTRACT This paper presents an actual application of the FITradeoff method to support strategic decisions on military budgets. Using Optimization Techniques and a particular Multicriteria Decision Aid method to assess budget allocation efficiency, a model was built for the Brazilian Defense Ministry. A case study of the Brazilian Navy is described in this paper, whereby this model was used. The focus is on the adaptations of FITradeoff necessary to make it suitable for different groups of decision-makers and staff, performing different tasks on the process. The FITradeoff proposal allowed a group decision with different levels suitable to the military context and high-level strategic decisions. The use of multicriteria in this approach provides a meaningful utility measure. This provides a score value representing the desirability of an alternative, in terms of its attainment of the institution’s strategic objectives. The approach contributed to an approximate gain of 15% in budget efficiency in 2022.

ABSTRACT Selecting the naval components to be part of a task force is a typical setting decision problem wherein each component of the task force should complement the others. This decision situation calls for the use of outranking modeling tools instead of the additive ones, which are usually used when selecting group components. This study applies Electre I to develop an original approach to this hypothetical problem: the composition of the Amphibian Readiness Group, used in the United States. Despite being a hypothetical decision situation, the work uses real data from American media. We use Electre I because it is a native outranking multicriteria decision method, designed to address choice decisions. Moreover, we take the support of The Visual Outdeck web app (Outranking Decision and Knowledge app) to implement the model and the Electre I multicriteria algorithm and a sensitivity analysis of the results regarding the weights and the agreement and disagreement cut-off levels. Further we apply the same input data to an additive decision algorithm, allowing comparison of the results. Consequently, we established the setting of the task force by utilizing elements that optimally complement the others in the task force configuration. Furthermore, the study finds that this task force differs from the one that should be constituted using traditional additive modeling. The main contribution of this study is that it provides a robust discussion and original modeling that supports military decision-makers while selecting a better set of military taskforces. ones Group States media method decisions Moreover Outranking cutoff cut off levels Consequently configuration Furthermore decisionmakers makers taskforces

ABSTRACT Despite medical advances, mortality due to acute coronary syndrome remains high. For this reason, it is important to identify the most critical factors for predicting the risk of death in patients hospitalized with this disease. To improve medical decisions, it is also helpful to construct models that enable us to represent how the main driving factors relate to patient outcomes. In this study, we compare the capability of Artificial Neural Network (ANN) and Support Vector Machine (SVM) models to distinguish between patients hospitalized with acute coronary syndrome who have low or high risk of death. Input variables are selected using the wrapper approach associated with a mutual information filter and two new proposed filters based on Euclidean distance. Because of missing data, the use of a filter is an important step in increasing the size of the usable data set and maximizing the performance of the classification models. The computational results indicate that the SVM model performs better. The most relevant input variables are age, any previous revascularization, and creatinine, regardless of the classification algorithms and filters used. However, the Euclidean filters also identify a second important group of input variables: age, creatinine and systemic arterial hypertension.