PROMETHEE-SAPEVO-M1 a Hybrid Approach Based on Ordinal and Cardinal Inputs: Multi-Criteria Evaluation of Helicopters to Support Brazilian Navy Operations

This paper presents a new approach based on Multi-Criteria Decision Analysis (MCDA), named PROMETHEE-SAPEVO-M1, through its implementation and feasibility related to the decision-making process regarding the evaluation of helicopters of attack of the Brazilian Navy. The proposed methodology aims to present an integration of ordinal evaluation into the cardinal procedure from the PROMETHEE method, enabling to perform qualitative and quantitative data and generate the criteria weights by pairwise evaluation, transparently. The modeling provides three models of preference analysis, as partial, complete, and outranking by intervals, along with an intra-criterion analysis by veto threshold, enabling the analysis of the performance of an alternative in a specific criterion. As a demonstration of the application, is carried out a case study by the PROMETHEE-SAPEVO-M1 web platform, addressing a strategic analysis of attack helicopters to be acquired by the Brazilian Navy, from the need to be evaluating multiple specifications with different levels of importance within the context problem. The modeling implementation in the case study is made in detail, first performing the alternatives in each criterion and then presenting the results by three different models of preference analysis, along with the intra-criterion analysis and a rank reversal procedure. Moreover, is realized a comparison analysis to the PROMETHEE method, exploring the main features of the PROMETHEE-SAPEVO-M1. Moreover, a section of discussion is presented, exposing some features and main points of the proposal. Therefore, this paper provides a valuable contribution to academia and society since it represents the application of an MCDA method in the state of the art, contributing to the decision-making resolution of the most diverse real problems.This research was funded by Centre for Research & Development in Mechanical Engineering (CIDEM), School of Engineering of Porto (ISEP), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto, Portugal.info:eu-repo/semantics/publishedVersio

A Systematic Approach to the Management of Military Human Resources through the ELECTRE-MOr Multicriteria Method

Personnel selection is increasingly proving to be an essential factor for the success of organizations. These issues almost universally involve multiple conflicting objectives, uncertainties, costs, and benefits in decision-making. In this context, personnel assessment problems, which include several candidates as alternatives, along with several complex evaluation criteria, can be solved by applying Multicriteria Decision Making (MCDM) methods. Uncertainty and subjectivity characterize the choice of personnel for missions or promotions at the military level. In this paper, we evaluated 30 Brazilian Navy officers in the light of four criteria and 34 subcriteria. To support the decision-making process regarding the promotion of officers, we applied the ELECTRE-Mor MCDM method. We categorized the alternatives into three classes in the modeling proposed in this work, namely: Class A (Promotion by deserving), Class B (Promotion by seniority), and Class C (Military not promoted). As a result, the method presented 20% of the officers evaluated with performance corresponding to class A, 53% of the alternatives to class B, and 26.7% with performances attributed to class C. In addition, we presented a sensitivity analysis procedure through variation of the cut-off level λ, allowing decision-making on more flexible or rigorous scenarios at the discretion of the Naval High Administration. This work brings a valuable contribution to academia and society since it represents the application of an MCDM method in state of the art to contribute to solving a real problem.info:eu-repo/semantics/publishedVersio

Design and implementation of a supplier Kanban system in the automotive sector: an empirical study

In an increasingly competitive automotive sector subject to rapid transformation, efficient supply chain management emerges as a critical component for success. In this context, this study explores a Kanban system within an automotive supplier to address the challenges of demand variability and inventory management. Employing an Action Research methodology, the case study unfolds four iterations, each advancing the Kanban solution towards greater efficiency and responsiveness. The initial phase identifies the critical issue of demand variability in part components, prompting a solution that integrates outlier analysis for forecast accuracy. Subsequent iterations refine this approach, evolving from fixed safety margins to a dynamic system based on service levels. A significant breakthrough is achieved in the latter stages, where the demand is aligned with the maximum machine consumption capacity, transitioning from weeks to days in lead time. This adjustment results in a notable reduction of Kanbans, effectively covering forecast peaks and optimizing space utilization. The final solution, entails a reconfiguration of storage racks tailored to the revised Kanban requirements. The outcome demonstrates a significant improvement in inventory management, aligning with the real-time production needs and showcasing the efficacy of a tailored Kanban system in a high variability environment.info:eu-repo/semantics/publishedVersio

SAPEVO-H² a Multi-Criteria Systematic Based on a Hierarchical Structure: Decision-Making Analysis for Assessing Anti-RPAS Strategies in Sensing Environments

Regarding high-level and complex decision-making scenarios, the study presents an extensive approach to the Simple Aggregation of Preferences Expressed by Ordinal Vectors-Multi Decision Making method (SAPEVO-M). In this context, the modeling proposal, named SAPEVO-Hybrid and Hierarchical (SAPEVO-H²), the objective of this study, based on the concepts of multi-criteria analysis, provides the evaluation of alternatives under the light of multiple criteria and perceptions, enabling the integration of the objectives of a problem, which are transcribed into attributes and structured in a hierarchical model, analyzing qualitative and quantitative data through ordinal and cardinal entries, respectively. As a case study, a decision analysis concerning the defense strategies against anti-Remotely Piloted Aircraft Systems (RPAS) strategies for the Brazilian Navy is carried out. Using the technique of the causal maps approach based on Strategic Options Development and Analysis (SODA) methodology, the problematic situation is structured for numerical implementation, demonstrating the performance of objectives and elements of a hierarchical structure. As a result, rankings concerning objectives and anti-RPAS technologies, based on the treatment of subjective information, are presented. In the end, the main contribution of the study and its limitations are discussed, along with the conclusions and some proposals for future studies

Systematic Literature Review on Virtual Electronics Laboratories in Education: Identifying the Need for an Aeronautical Radar Simulator

The objective of this work is to propose the development of a virtual electronics laboratory with an aeronautical radar simulator using immersive technologies to help students learn. To verify whether this proposal was viable, the systematic literature review (SLR) methodology was used, whose objective was to verify whether immersive technologies were being used effectively in education and, also, what challenges, opportunities, and benefits they bring to Education 4.0. For this, eight Research Questions (RQs) were formulated to be answered by articles based on the highest SLR scores. The results presented by SLR were as follows: there was an increase in the use of immersive technologies in education, but virtual reality (VR) is still more used in education than AR, despite VR being more expensive than AR; the use of these new technologies brings new challenges, opportunities, and benefits for education; there was an increase in the quality of teaching for complex subjects; and there was an increase in students’ interest in the content presented

Optimization of Obstructive Sleep Apnea Management: Novel Decision Support via Unsupervised Machine Learning

This study addresses Obstructive Sleep Apnea (OSA), which impacts around 936 million adults globally. The research introduces a novel decision support method named Communalities on Ranking and Objective Weights Method (CROWM), which employs principal component analysis (PCA), unsupervised Machine Learning technique, and Multicriteria Decision Analysis (MCDA) to calculate performance criteria weights of Continuous Positive Airway Pressure (CPAP—key in managing OSA) and to evaluate these devices. Uniquely, the CROWM incorporates non-beneficial criteria in PCA and employs communalities to accurately represent the performance evaluation of alternatives within each resulting principal factor, allowing for a more accurate and robust analysis of alternatives and variables. This article aims to employ CROWM to evaluate CPAP for effectiveness in combating OSA, considering six performance criteria: resources, warranty, noise, weight, cost, and maintenance. Validated by established tests and sensitivity analysis against traditional methods, CROWM proves its consistency, efficiency, and superiority in decision-making support. This method is poised to influence assertive decision-making significantly, aiding healthcare professionals, researchers, and patients in selecting optimal CPAP solutions, thereby advancing patient care in an interdisciplinary research context

Feasibility of a Hospital Information System for a Military Public Organization in the Light of the Multi-Criteria Analysis

The healthcare environment presents a large volume of personal and sensitive patient data that needs to be available and secure. Information and communication technology brings a new reality to healthcare, promoting improvements, agility and integration. Regarding high-level and complex decision-making scenarios, the Brazilian Navy (BN), concerning its healthcare field, is seeking to provide better management of its respective processes in its hospital facilities, allowing accurate control of preventive and curative medicine to members who work or have served there in past years. The study addresses the understanding, structure and clarifying variables related to the feasibility of technological updating and installing of a Hospital Information System (HIS) for BN. In this scenario, through interviews and analysis of military organization business processes, criteria and alternatives were established based on multi-criteria methodology as a decision aid. As methodological support for research and data processing, THOR 2 and PROMETHEE-SAPEVO-M1 methods were approached, both based on the scenarios of outranking alternatives based on the preferences established by the stakeholders in the problem. As a result of the methodological implementation, we compare the two implemented methods in this context, exposing the Commercial Software Purchase and Adoption of Free Software, integrated into Customization by the Marine Studies Foundation, as favorable actions to be adopted concerning HIS feasibility. This finding generates a comprehensive discussion regarding the BN perspective and changes in internal development in the military environment, prospecting alignment to the culture of private organizations in Information Technology for healthcare management. In the end, we present some conclusions concerning the study, exploring the main points of the decision-making analysis and for future research

Advancing Efficiency Sustainability in Poultry Farms through Data Envelopment Analysis in a Brazilian Production System

The production efficiency factor is widely used to measure the zootechnical performance of a batch of broilers. The unit cost of production brings new elements to improve efficiency evaluation and financial sustainability for this activity in agriculture. This research aims to evaluate the production efficiency level of the crop to maximize the return on investment. This study uses Data Envelopment Analysis (DEA) with the computational processing of the SIAD software (Integrated Decision Support System). The variables selected were poultry housing, age at slaughter, feed consumed, mortality, and unit cost. The chosen output variable was the total available weight. The analysis spans 31 decision-making units (DMUs) composed of integrated producers, unveiling a frontier of efficiency delineated by the most exemplary DMUs. Notably, only two DMUs, specifically DMU 4 and DMU 23, approached the threshold of maximum relative efficiency. This research illuminates the critical role of unit cost in enhancing the assessment of production efficiency and financial sustainability within the agriculture environment. By setting benchmarks for efficient management and operational protocols, our findings serve as a cornerstone for improving practices among less efficient DMUs, contributing significantly to the literature on agricultural efficiency and offering actionable insights for the poultry farming sector

Integrating multicriteria decision making and principal component analysis: a systematic literature review

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