http://repositorio.ufc.br/handle/riufc/165 2026-05-31T02:48:42Z http://repositorio.ufc.br/handle/riufc/86136 Título: Algorítimo de busca em vizinhança com q-learning para a resolução do no-idle flow shop para minimização do atraso total Autor(es): Ramos, Diego Guilherme Ferreira Abstract: This work addresses the No-Idle Permutation Flow Shop Problem (NIPFSP) to Minimize Total Tardiness. Initially, a Mixed Integer Linear Programming (MILP) model and a Mixed-Integer Quadratically Constrained Programming (MIQCP) model were proposed, aiming to structure the modeling foundations of the problem. The MILP and MIQCP models were computationally implemented, and their characteristics were validated. Exact experiments revealed the effectiveness of the MILP model in producing high-quality solutions for instances with the number of jobs ranging between 20 and 100 and with up to 5 machines. However, for problems with 10, 15, and 20 machines, the MILP model was unable to prove optimality within the established time limit, pointing to the need for approximate methods. Therefore, a hybrid metaheuristic was proposed that combines intensive local search with reinforcement learning (Q-learning) to adaptively explore the solution space in more difficult instances, named Iterated Noisy Local Search Q-learning (INLSQL), which was compared with other existing heuristic algorithms. Substantial performance gains were achieved through the Q-learning-guided strategy compared to the main algorithms reported in the reviewed literature. Tipo: Dissertação 2026-03-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/86088 Título: Uma visão sobre a gestão escolar no desempenho educacional no Ceará: uma análise quantitativa da evolução do SAEB (2005-2023) e do ICG (2023) Autor(es): Maia, Priscila de Sousa Cunha Abstract: This dissertation investigates the relationship between school performance indicators and the characteristics associated with school management in the state of Ceará, based on data from the Basic Education Assessment System (SAEB) and the School Census from 2005 to 2023, as well as the School Management Complexity Indicator (ICG), used exclusively for the year 2023. The study seeks to understand how effective management practices, aligned with public policies focused on learning, influence the results of Portuguese Language and Mathematics tests, considering the proficiency averages of municipal networks in SAEB basic education. A quantitative approach is adopted, with analysis of secondary data extracted from the National Institute for Educational Studies and Research Anísio Teixeira (INEP), applying descriptive statistics, correlations, analysis of variance (ANOVA), and least significant difference (LSD). The analysis highlights the municipality of Fortaleza, enabling comparisons with other municipal networks in Ceará. The results indicate that although Ceará has shown significant progress—possibly driven by programs such as the Literacy at the Right Age Program (PAIC)—Fortaleza performed below the state average. Conversely, smaller unicipalities, including those in rural areas, achieved higher averages, demonstrating that a well-structured pedagogical management can offset limitations related to scale and resources. The analysis of variance (ANOVA) reinforced the lower internal inequality in Ceará compared with the national pattern, while Fortaleza showed better performance in low-complexity schools. These findings suggest that improvements in learning are associated with pedagogical leadership, systematic monitoring of results, and management capacity responsive to the school context, providing concrete insights for public school systems seeking to promote equity and quality in education. Tipo: Dissertação 2025-01-01T00:00:00Z 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 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 2026-01-01T00:00:00Z