Metaheurística GRASP para o problema de agrupamento de alunos em escolas

Detalhes bibliográficos
Autor(a) principal: Sá, Nayza Mamede
Data de Publicação: 2025
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
Texto Completo: http://repositorio.ufc.br/handle/riufc/80785
Resumo: The efficient allocation of students to the schools closest to their residences is crucial, considering school capacities and individual student demands.This problem can be seen as a capacitated clustering problem, where schools have different capacities for each grade. Given that this class of problems is NP-hard, developing approximate algorithms is essential for solving medium and large-scale instances. This research proposes and analyzes methods to solve the Students Clustering Problem (SCP). Computational experiments conducted on a set of 120 randomly generated instances demonstrated the necessity of using metaheuristics, as the exact model was unable to obtain integer solutions within a time limit of 300 seconds. To address this limitation, a constructive heuristic and three metaheuristics were implemented: Clustering Search and A-BRKGA, which have been proposed in the literature for related problems, as well as GRASP, developed specifically for this study. The results indicate that GRASP outperformed the other methods, obtaining high-quality solutions in reduced computational times, establishing itself as a viable alternative for solving the SCP. Furthermore, its application in a real-world case study demonstrated the method’s effectiveness in optimizing student allocation and improving the distribution of available school slots. Thus, this research contributes to the development of an efficient approach with the potential to support public policies aimed at educational management.
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spelling Sá, Nayza MamedePrata, Bruno de Athayde2025-05-08T19:01:31Z2025-05-08T19:01:31Z2025-03-13SÁ, Nayza Mamede. Metaheurística GRASP para o problema de agrupamento de alunos em escolas. 2025. 70 f. Dissertação (Mestrado em Modelagem e Métodos Quantitativos) - Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2025.http://repositorio.ufc.br/handle/riufc/80785The efficient allocation of students to the schools closest to their residences is crucial, considering school capacities and individual student demands.This problem can be seen as a capacitated clustering problem, where schools have different capacities for each grade. Given that this class of problems is NP-hard, developing approximate algorithms is essential for solving medium and large-scale instances. This research proposes and analyzes methods to solve the Students Clustering Problem (SCP). Computational experiments conducted on a set of 120 randomly generated instances demonstrated the necessity of using metaheuristics, as the exact model was unable to obtain integer solutions within a time limit of 300 seconds. To address this limitation, a constructive heuristic and three metaheuristics were implemented: Clustering Search and A-BRKGA, which have been proposed in the literature for related problems, as well as GRASP, developed specifically for this study. The results indicate that GRASP outperformed the other methods, obtaining high-quality solutions in reduced computational times, establishing itself as a viable alternative for solving the SCP. Furthermore, its application in a real-world case study demonstrated the method’s effectiveness in optimizing student allocation and improving the distribution of available school slots. Thus, this research contributes to the development of an efficient approach with the potential to support public policies aimed at educational management.A alocação eficiente dos alunos às escolas mais próximas de suas residências é crucial, considerando as capacidades escolares e as demandas individuais dos alunos. Tal problema pode ser visto como um problema de agrupamento capacitado no qual as escolas possuem capacidades diferentes para cada série. Tendo em vista que esta classe de problemas é NP-difícil, a proposição de algoritmos aproximados, é de suma relevância para a resolução de instâncias de médio e grande porte. Nesta pesquisa, propõe-se e analisa-se métodos para a resolução do Problema de Agrupamento de Alunos em Escolas (Students Clustering Problem - SCP). Experimentos computacionais conduzidos em um conjunto de 120 instâncias geradas aleatoriamente demonstraram a necessidade do uso de metaheurísticas, uma vez que o modelo exato não foi capaz de obter soluções inteiras dentro de um limite de tempo de 300 segundos. Para abordar essa limitação, foram implementadas uma heurística construtiva e três metaheurísticas: Clustering Search e A-BRKGA, propostas na literatura para problemas correlatos, além do GRASP, desenvolvido especificamente para este estudo. Os resultados indicam que o GRASP apresentou desempenho superior, obtendo soluções de alta qualidade em tempos computacionais reduzidos, consolidando-se como uma alternativa viável para a resolução do SCP. Além disso, sua aplicação em um estudo de caso real demonstrou a eficácia do método na otimização da alocação de alunos, resultando na melhoria da distribuição das vagas nas escolas. Dessa forma, esta pesquisa contribui para o desenvolvimento de uma abordagem eficiente, com potencial para apoiar políticas públicas voltadas à gestão educacional.Metaheurística GRASP para o problema de agrupamento de alunos em escolasGRASP metaheuristic for the problem of grouping students in schoolsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisOtimização combinatóriaProgramação linear inteiraAnálise por agrupamentoMetaheurística GRASPEstudantes de ensino fundamentalCombinatorial optimizationInteger linear programmingClustering analysisGRASP metaheuristicElementary school studentsCNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICAinfo:eu-repo/semantics/openAccessporreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFChttp://lattes.cnpq.br/1966662311797648https://orcid.org/0000-0002-3920-089Xhttp://lattes.cnpq.br/99570401646974102025-03ORIGINAL2025_dis_nmsa.pdf2025_dis_nmsa.pdfDissertação Versão Finalapplication/pdf2548533http://repositorio.ufc.br/bitstream/riufc/80785/3/2025_dis_nmsa.pdfb7d751dc6987372f691b1743bf7b9bd4MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.ufc.br/bitstream/riufc/80785/4/license.txt8a4605be74aa9ea9d79846c1fba20a33MD54riufc/807852025-05-08 16:01:32.569oai:repositorio.ufc.br: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Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2025-05-08T19:01:32Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false
dc.title.pt_BR.fl_str_mv Metaheurística GRASP para o problema de agrupamento de alunos em escolas
dc.title.en.pt_BR.fl_str_mv GRASP metaheuristic for the problem of grouping students in schools
title Metaheurística GRASP para o problema de agrupamento de alunos em escolas
spellingShingle Metaheurística GRASP para o problema de agrupamento de alunos em escolas
Sá, Nayza Mamede
CNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
Otimização combinatória
Programação linear inteira
Análise por agrupamento
Metaheurística GRASP
Estudantes de ensino fundamental
Combinatorial optimization
Integer linear programming
Clustering analysis
GRASP metaheuristic
Elementary school students
title_short Metaheurística GRASP para o problema de agrupamento de alunos em escolas
title_full Metaheurística GRASP para o problema de agrupamento de alunos em escolas
title_fullStr Metaheurística GRASP para o problema de agrupamento de alunos em escolas
title_full_unstemmed Metaheurística GRASP para o problema de agrupamento de alunos em escolas
title_sort Metaheurística GRASP para o problema de agrupamento de alunos em escolas
author Sá, Nayza Mamede
author_facet Sá, Nayza Mamede
author_role author
dc.contributor.author.fl_str_mv Sá, Nayza Mamede
dc.contributor.advisor1.fl_str_mv Prata, Bruno de Athayde
contributor_str_mv Prata, Bruno de Athayde
dc.subject.cnpq.fl_str_mv CNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
topic CNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
Otimização combinatória
Programação linear inteira
Análise por agrupamento
Metaheurística GRASP
Estudantes de ensino fundamental
Combinatorial optimization
Integer linear programming
Clustering analysis
GRASP metaheuristic
Elementary school students
dc.subject.ptbr.pt_BR.fl_str_mv Otimização combinatória
Programação linear inteira
Análise por agrupamento
Metaheurística GRASP
Estudantes de ensino fundamental
dc.subject.en.pt_BR.fl_str_mv Combinatorial optimization
Integer linear programming
Clustering analysis
GRASP metaheuristic
Elementary school students
description The efficient allocation of students to the schools closest to their residences is crucial, considering school capacities and individual student demands.This problem can be seen as a capacitated clustering problem, where schools have different capacities for each grade. Given that this class of problems is NP-hard, developing approximate algorithms is essential for solving medium and large-scale instances. This research proposes and analyzes methods to solve the Students Clustering Problem (SCP). Computational experiments conducted on a set of 120 randomly generated instances demonstrated the necessity of using metaheuristics, as the exact model was unable to obtain integer solutions within a time limit of 300 seconds. To address this limitation, a constructive heuristic and three metaheuristics were implemented: Clustering Search and A-BRKGA, which have been proposed in the literature for related problems, as well as GRASP, developed specifically for this study. The results indicate that GRASP outperformed the other methods, obtaining high-quality solutions in reduced computational times, establishing itself as a viable alternative for solving the SCP. Furthermore, its application in a real-world case study demonstrated the method’s effectiveness in optimizing student allocation and improving the distribution of available school slots. Thus, this research contributes to the development of an efficient approach with the potential to support public policies aimed at educational management.
publishDate 2025
dc.date.accessioned.fl_str_mv 2025-05-08T19:01:31Z
dc.date.available.fl_str_mv 2025-05-08T19:01:31Z
dc.date.issued.fl_str_mv 2025-03-13
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.fl_str_mv SÁ, Nayza Mamede. Metaheurística GRASP para o problema de agrupamento de alunos em escolas. 2025. 70 f. Dissertação (Mestrado em Modelagem e Métodos Quantitativos) - Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2025.
dc.identifier.uri.fl_str_mv http://repositorio.ufc.br/handle/riufc/80785
identifier_str_mv SÁ, Nayza Mamede. Metaheurística GRASP para o problema de agrupamento de alunos em escolas. 2025. 70 f. Dissertação (Mestrado em Modelagem e Métodos Quantitativos) - Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2025.
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