http://repositorio.ufc.br/handle/riufc/71 Wed, 08 Apr 2026 15:16:50 GMT 2026-04-08T15:16:50Z http://repositorio.ufc.br/handle/riufc/85562 Título: Uma proposta metodológica para distribuição de recursos do PDDE a partir de critérios de eficiência e necessidade com aplicação de análise envoltória de dados Autor(es): Fossile, Marcelo Gomes Abstract: This study proposes a methodology for the allocation of resources from the Programa Dinheiro Direto na Escola (PDDE) among Brazilian states, guided by criteria of technical efficiency and need, using Data Envelopment Analysis (DEA) as a supporting tool for measuring relative performance. The analysis considers a financial input and two sets of outputs: structural variables expressed in absolute values, which reflect the installed capacity of education systems, and educational indicators expressed as ratios, which capture performance outcomes. The models were implemented in Python using the Pyomo library and solved with the GLPK solver. Based on the efficiency scores, the potential savings associated with projecting units onto the efficient frontier are estimated and used as a reference for defining the amount of resources to be allocated, which may be adjusted according to criteria established by public managers. In the subsequent stage, a parametric model is developed that combines relative efficiency with a need index composed of eight indicators, weighted by the parameter λ ∈ [0,1], allowing the simulation of scenarios ranging from efficiency-oriented allocation to greater emphasis on vulnerability. The results reveal distinct territorial patterns in both efficiency and levels of need, highlighting how different weighting choices between these two dimensions affect the final allocation of resources. The proposed approach provides a transparent quantitative tool to support public policy decisions aimed at optimizing federal investments in basic education. Tipo: Dissertação Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/85562 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/85479 Título: Decisão multicritério orientada por ênfases do decisor: uma abordagem estatística para decisões alinhadas a diretrizes organizacionais Autor(es): Ferreira, José Robério de Castro Abstract: Several decision-making problems involve the analysis of multiple criteria that are often conflicting with one another. In this context, the resulting scenarios are complex, which calls for systematic methods to find solutions that adequately address all the criteria involved. The socalled multicriteria decision support methods can be used to assist in solving this type of problem. Among the various applicable methods, the AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) stand out as the most widely used in the literature, along with numerous studies that propose variations and enhancements of these methods to expand their applicability across different decision contexts. This study proposes three new methodological approaches for multicriteria decision-making focused on emphasizing preferable criteria, namely the AHP-Equitative, AHP-MEDAC, and Hierarchical TOPSIS-MEDAC methods. These approaches aim to increase the weights of preferable criteria through the application of statistical measures to the performance of the alternatives in each criteria, specifically skewness and variability are used. The AHP-Equitative, inspired by the Gaussian AHP method, was the first approach proposed in this research, and some of its statistical limitations inherited from the Gaussian AHP are addressed in the AHP-MEDAC method. To validate the proposed methods (AHP-Equitative, AHP-MEDAC and Hierarchical TOPSISMEDAC), they were applied within the educational context, resulting in a ranking constructed based on emphases previously defined by the decision-maker (promotion of equity), maintaining coherence and alignment with the institutional guidelines, policies, and strategic objectives that underpin any multicriteria decision process. The results were satisfactory, demonstrating an increase in the weights of the emphasized criteria and greater contrasts among the scores of the alternatives, which tend to mitigate the level of criticality in decision-making. Moreover, the methods demonstrated robustness across different modeling scenarios. Tipo: Dissertação Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/85479 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/83885 Título: Modelos Gamlss para fluxos comerciais: uma abordagem alternativa à gravitação clássica Autor(es): Vieira, Yasmin Mara dos Santos Abstract: The gravity model is frequently used to describe international trade flows, but it faces challenges when dealing with data characterized by heteroskedasticity, asymmetry, and excess zeros. This study analyzes the effectiveness of Generalized Additive Models for Location, Scale, and Shape (GAMLSS) in modeling such flows, compared to conventional estimators such as the Poisson Pseudo-Maximum Likelihood (PPML) and the Gamma Pseudo-Maximum Likelihood (GPML). The empirical application uses data on Brazilian exports of carnauba wax—a product of economic and strategic relevance—considering both economic and institutional factors that influence trade. Models with continuous and zero-inflated distributions were estimated, highlighting the Zero Adjusted Gamma (ZAGA) distribution, which showed the best performance according to information criteria and residual diagnostics. The results indicate that although PPML and GPML are consistent in certain contexts, both displayed limitations when facing high variability and the presence of zeros. In contrast, the GAMLSS (ZAGA) model achieved superior performance, allowing for the simultaneous modeling of the mean, dispersion, and probability of zero trade flows. From a substantive standpoint, the main effects predicted by gravity theory were confirmed, including the positive impact of the importers’ GDP and population, as well as the negative effects of distance, exchange rate, and lack of coastline. It was also found that regional agreements, although not increasing the average export volume, contributed to reducing its variability and the frequency of zero-trade periods, acting as stabilizing instruments for trade relations. The main contribution of this research is to demonstrate how GAMLSS models are robust and flexible tools for analyzing trade flows, expanding the range of methodologies available for studies in international economics. Tipo: Dissertação Sat, 01 Nov 2025 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/83885 2025-11-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/83312 Título: Escalonamento de projetos com restrições de recursos: modelagem matemática e aplicações práticas na gestão de projetos Autor(es): Rodrigues, Will Anderson Martins Abstract: Project scheduling under resource constraints is a recurrent challenge in contemporary project management, demanding effective methods to simultaneously optimize time, cost, and quality in complex environments. Traditional techniques such as PERT (Program Evaluation and Review Technique) and CPM (Critical Path Method) show significant limitations in handling multiple execution modes and dynamic variations in resource availability. In this context, this study investigates how mathematical modeling combined with advanced metaheuristics can enhance project scheduling, enabling a more efficient and adaptive allocation of available resources. The methodology adopted was a systematic literature review, covering publications from the past ten years in prominent scientific databases such as CAPES, SciELO, and IEEE Xplore. Relevant articles addressing the classical Resource- Constrained Project Scheduling Problem (RCPSP) were analyzed, with special attention to heuristic and metaheuristic techniques, including genetic algorithms, tabu search, and particle swarm optimization (PSO). The findings indicate that hybrid approaches, particularly those integrating evolutionary algorithms with local search techniques, consistently outperform traditional methods, especially regarding flexibility and adaptability to unforeseen project changes. Additionally, emerging trends identified in the literature suggest that integrating machine learning techniques could potentially reduce computational complexity and enhance the effectiveness of the employed models. The study concludes that combining mathematical modeling with advanced heuristics represents a significant advancement, not only in optimizing resource allocation but also in fostering innovative computational strategies, particularly applicable to sectors such as construction, manufacturing, and information technology. Tipo: Dissertação Tue, 01 Apr 2025 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/83312 2025-04-01T00:00:00Z