Navy Warship Selection and Multicriteria Analysis: The THOR Method Supporting Decision Making

Frontiers in Artificial Intelligence and Applications

et al. 2021

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3D printing technologies define the essence of Additive Manufacturing and make possible the agile production of customized parts from different materials, with lower unit cost and waste generation. Currently, one of the most widespread 3D printer technologies is the Fused Deposition Modeling (FDM) type, which is the object of this paper. The choice of 3D printing equipment depends on the alignment of the purpose of use and technical knowledge to consider certain requirements. Therefore, this choice can be time-consuming and/or imprecise. In this sense, this work aimed to classify FDM-type 3D printer models by applying the ELECTRE-MOr method, a Multi-criteria Decision Aiding (MCDA) method. As a result, based on a categorization between classes, the FABER 10 alternative was the only one that presented class A performance in all evaluated scenarios, based on criteria defined by the experts consulted in this study.

Section: The Electre-mor Methodsmentioning

confidence: 99%

Multicriteria Analysis in Additive Manufacturing: An ELECTRE-MOr Based Approach

Drumond

Frontiers in Artificial Intelligence and Applications

et al. 2021

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“…Step 4 -Calculation of relative dab dominance. Obtained by the weighted sum of the criteria weights (wj), with the corresponding fraction (σj(ab)) verified in the preference modeling (5):…”

Section: Table 4 Criteria Preference Modelingmentioning

confidence: 99%

Investments in Times of Pandemics: An Approach by the SAPEVO-M-NC Method

Maêda

Frontiers in Artificial Intelligence and Applications

et al. 2021

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The consequences of the pandemic caused by the new coronavirus in the most diverse sectors of the Brazilian economy, are overwhelming, and its effects are still difficult to measure completely. There are several possible scenarios being considered, such as prolonged depression, “U” or “V” recovery. Due to such volatility, risks and uncertainties, the investor, before investing, must carefully analyze the alternatives available in the market. Given the above, this article aims to propose different ways of distributing a financial portfolio, considering five investment funds, which were evaluated in the light of five criteria, by two investors who work in the financial market. Therefore, the SAPEVO-M-NC multicriteria decision aid method was used to evaluate the alternatives, as well as their composition in the investment portfolios. The adoption of the methodology made it possible to carry out the distribution of the portfolio in a clear and consistent way, showing itself as an efficient practical tool for the proposed approach.

“…Multicriteria methods consider value judgments and not only technical issues [4], and tend to be increasingly adopted to address the real-world construction problems [5]. These methods have been used to support the decision-making process in several recent complex problems, as presented in [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Algorithm Selection for Machine Learning Classification: An Application of the MELCHIOR Multicriteria Method

Frontiers in Artificial Intelligence and Applications

Maêda

et al. 2021

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This paper aims to select an algorithm for the Machine Learning (ML) classification task. For the proposed analysis, the Multi-criteria Decision Aid (MCDA) Méthode d’ELimination et de CHoix Includent les relations d’ORdre (MELCHIOR) method was applied. The experiment considered the following criteria as relevant: Accuracy, sensitivity, and processing time of the algorithms. The data used refers to the intention of buying on the Internet and the purpose is to predict whether the customer will finalize a particular purchase. Among various MCDA techniques available, MELCHIOR was chosen to support the decision-making process because this method provides the evaluation of alternatives without the need to elicit the weights of the criteria. As a result, the Gradient Boosting Decision Tree algorithm has been selected as the most suitable for the ML classification task.