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The selection of a navy ship is essential to guarantee a country's sovereignty, deterrence capabilities, and national security, especially in the face of possible conflicts and diplomatic instability. This paper proposes the integration of concepts related to multi-criteria decision making (MCDM) methodology and machine learning, creating the Simple Aggregation of Preferences Expressed by Ordinal Vectors—Principal Components (SAPEVO-PC) method. The proposed method proposes an evolution of the SAPEVO family, allowing the inclusion of qualitative preferences, and adds concepts from Principal Component Analysis (PCA), aiming to simplify the decision-making process, maintaining precision and reliability. We carried out a case study analyzing 32 warships and ten quantitative criteria, demonstrating the practical application and effectiveness of the method. The generated rankings reflected both subjective perceptions and the quantitative performance data of each ship. This innovative integration of qualitative data with a quantitative machine learning algorithm ensures comprehensive and robust analyses, facilitating informed and strategic decisions. The results showed a high degree of consistency and reliability, with the top and bottom rankings remaining stable across different decision-makers' perspectives. This study highlights the potential of SAPEVO-PC to improve decision-making efficiency in complex, multi-criteria environments, contributing to the field of marine science. [ABSTRACT FROM AUTHOR]

Corn crops can be irrigated using surface irrigation methods (furrows, horizontal pots, strips), as well as fixed or mobile sprinklers under canopy and localized irrigation (drip and micro sprinklers). The selection of the most appropriate irrigation method and system for a particular situation depends on an assessment of the available physical resources, such as water supply (quantity, quality and location), soil properties (texture, structure, depth, salinity, drainage, topography, erodibility) and plant climate systems, in which the adaptability of crops to the climatic conditions of each site must be taken into account. In this sense, the objective of this work is to select an irrigation method for a corn plantation in a Local Productive Arrangement (LPA). For this study, six irrigation methods were pre-selected as alternatives, and seven pre-established comparison criteria were unstructured interviews with stakeholders. The solution of the problem was carried out using the Multicriteria Decision Analysis model Simple Aggregation of Preferences Expressed by Ordinal Vectors – Multi Decision Makers (SAPEVO-M). After modeling the problem with the decision-makers, the method indicated the drip irrigation system as the best alternative. [ABSTRACT FROM AUTHOR]

Decision-support methods are crucial for analyzing complex alternatives and criteria in today's data-driven world. This Systematic Literature Review (SLR) explores and synthesizes knowledge about decision support methodologies that integrate Multicriteria Decision Making (MCDM) and Principal Component Analysis (PCA), an unsupervised Machine Learning (ML) technique. Both techniques optimize complex decisions by combining multiple criteria and dimensional data analysis. Focusing on performance evaluations, criterion weighting, and validation testing, this review identifies significant gaps in existing methodologies. These include the lack of consideration for non-beneficial criteria in PCA, insufficient validation tests in over half of the studies, and the non-use of communalities (the contribution of each criterion to the main factors) in decision support approaches. Additionally, this SLR offers a comprehensive quantitative overview, analyzing data from the Scopus, IEEE, and Web of Science databases and identifying 16 relevant studies. Furthermore, the scarcity of systematic reviews integrating MCDM and PCA techniques impedes evidence-based decision- making practices and theoretical evolution. This is particularly crucial as ML and data analysis advance rapidly, requiring models that reflect technological innovations. This article addresses this gap in the literature by providing an analysis of decision support methods and guiding further improvement in this field. [ABSTRACT FROM AUTHOR]

Concerning the development of computational tools and solutions as a decision-making aid, this paper presents the results of the waspasWEB project, which strives to provide decision-makers with a readily accessible mechanism to employ the weighted aggregated sum product assessment (WASPAS) method. The social contribution of the project encompasses the development of a user-friendly and publicly accessible internet tool, as well as a package launched on the Comprehensive R Archive Network (CRAN) to serve the community of users of the R language. The use of operational research methodologies is crucial to justify decisions, and this effort seeks to advance the adoption of such methodologies, offering managers, researchers, and the general public an intuitive and easily accessible multi-criteria decision-making tool. In this way, we present the technical specifications, usability, and interactivity of the user with the computational platform, being validated its viability through a hypothetical case study. At the end of the research, it exposes the limitations and feasibility of the proposed computational model along with future research. [ABSTRACT FROM AUTHOR]

This paper proposes the use of the AHP-Gaussian method to support the selection of a smart sensor installation for an electric motor used in an escalator in a subway station. The AHP-Gaussian methodology utilizes the Analytic Hierarchy Process (AHP) framework and is highlighted for its ability to save the decision maker's cognitive effort in assigning weights to criteria. Seven criteria were defined for the sensor selection: temperature range, vibration range, weight, communication distance, maximum electric power, data traffic speed, and acquisition cost. Four smart sensors were considered as alternatives. The results of the analysis showed that the most appropriate sensor was the ABB Ability smart sensor, which scored the highest in the AHP-Gaussian analysis. In addition, this sensor could detect any abnormalities in the equipment's operation, enabling timely maintenance and preventing potential failures. The proposed AHP-Gaussian method proved to be an effective approach for selecting a smart sensor for an electric motor used in an escalator in a subway station. The selected sensor was reliable, accurate, and cost-effective, contributing to the safe and efficient operation of the equipment. [ABSTRACT FROM AUTHOR]

Everywhere in the world, in various scenarios and situations, it is necessary to deal with the need to make decisions. In the context of public management, this need is often seen. However, this process is not always finished with satisfactory and positive results. It was proposed to use the hybrid method to support decision-making AHP-TOPSIS-2N, which is the combination of the AHP (Analytic Hierarchy Process) method with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to assist the decision maker to improve the control of public spending. Several studies were presented that prove the veracity and effectiveness of the method being used in different contexts. The present study proved to be effective in presenting the method as a great tool that can assist public management in the bidding processes, and thus ensure results with greater transparency and effect, in addition to presenting to society favorable results with the use of this systematized and structured method. [ABSTRACT FROM AUTHOR]

The vehicle selection problem for the armed forces is a very important problem. In order to make the best decision regarding the selection of the best alternative among the existing options is necessary to use support decision-making tools. The aim of this paper is to demonstrate the functionality of the TOPSIS-M method in a vehicle selection problem for the Brazilian Navy (Marinha do Brasil). Among the vehicles selected as alternatives, the best option, according to the method used, was the vehicle IVECO HI-WAY800S56TZ. According to this work, the method used for vehicle selection proved to be efficient in assisting decision-making for the acquisition of new vehicles for the Brazilian Navy. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to suggest the expected demand for the sale of chains, earrings, bracelets, anklets, rings, and various accessories from e-commerce for one year using the AnyLogistix simulation tool. The study was applied to the company Marinna Acessórios and contributes so that those responsible for the strategic planning of the enterprise can have a starting point on sales and which cities should direct the greatest efforts, since the model that performs the forecast based on population density was used. At the end of the application, it is possible to verify the 25 cities where financial returns can be higher and the expected amount of each product for each city. The result generated is of great value for the company that is at the beginning of its operations and does not have any demand history that could be used to make the forecasts. [ABSTRACT FROM AUTHOR]

Related to the Internet of Things (IoT) scenario, the research in question brings a proposal of multi-adaptive sensing technology integrated into the data analysis environment as decision support in commercial and industrial management scenarios. The given study exposes a proposal of an adaptive multi-sensor, operable in monitoring temperature, movement, pressure, vibration, and rotation, which is technically presented without hardware-specific knowledge. The study presents the main characteristics of the sensing device and its functionalities and feasibilities when integrated into a web computing platform, aimed at processing, and processing the captured data, converted into information and analysis to support the decision at the organizational level. At the end of the study, is presented the considerations under the research and proposals for future studies. [ABSTRACT FROM AUTHOR]