Student Allocation Systems in Schools: A Systematic Literature Review
DOI:
https://doi.org/10.5753/rbie.2026.6639Keywords:
Allocation Systems, Student Allocation, Schools, Systematic Literature ReviewAbstract
Allocating students to public schools is a logistical and social challenge with direct effects on equity, access, and educational quality, especially in contexts of uneven demand and capacity constraints. This article presents a literature review of student–school allocation systems covering the period from 2014 to 2024. The review includes national and international studies that propose different approaches to the problem, including models based on preferences, distance, school capacity, and socioeconomic composition. It also examines the main algorithms employed, such as stable matching, heuristic and metaheuristic methods, among others. The synthesis highlights recurring objectives, including minimizing distances and costs, ensuring stable allocations, and achieving efficient capacity utilization, and discusses practical barriers: data quality and integration, rule transparency, social acceptance, territorial inequalities, and large-scale computational costs. The aim is to provide a comprehensive overview of proposed solutions and their implications for designing fairer and more efficient public policies in education.
Downloads
References
Ali, A. Y. (2021). Greedy algorithm for solving student allocation problem in internship program: a case study. International journal of research in industrial engineering, 10(2), 155–164. https://doi.org/10.22105/riej.2021.266125.1179 [GS Search].
Alifi, M. R., Hayati, H., & Supangkat, S. H. (2017). Optimization of school network using location-allocation analysis: Case study: Bandung, Indonesia. 2017 IEEE Region 10 Symposium (TENSYMP), 1–6. https://doi.org/10.1109/TENCONSpring.2017.8070099 [GS Search].
Arnosti, N. (2023). Lottery design for school choice. Management Science, 69(1), 244–259. https://doi.org/10.1287/mnsc.2022.4338 [GS Search].
Badoni, R. P., & Gupta, D. (2015). A new algorithm based on students groupings for university course timetabling problem. 2015 2nd international conference on recent advances in engineering & computational sciences (RAECS), 1–5. https://doi.org/10.1109/RAECS.2015.7453340 [GS Search].
Bouzarth, E. L., Forrester, R., Hutson, K. R., & Reddoch, L. (2018). Assigning students to schools to minimize both transportation costs and socioeconomic variation between schools. Socio-Economic Planning Sciences, 64, 1–8. https://doi.org/10.1016/j.seps.2017.09.001 [GS Search].
Delgado, V. M. S. (2013). Alocação dos alunos nas escolas: uma abordagem de algoritmos de pareamento para análise do efeito do cadastro escolar de Belo Horizonte na proficiência dos estudantes. [GS Search] [Link].
Fernandes, G. A. A. L. (2018). O problema das matrículas escolares: uma discussão sobre a melhoria das regras utilizadas em São Paulo. Revista de Administração Pública, 52(4), 740–752. https://doi.org/10.1590/0034-7612163466 [GS Search].
Ilham, M. N., Arbansyah, A., Suryawan, S. H., & Wirayuda, P. (2020). Application of Bubble Sort Optimization in New Student Admission Selection Using Brute Force Algorithm. Tepian, 5(2), 568771. https://doi.org/10.51967/tepian.v5i2.3055 [GS Search].
Jiang, Y., Li, B., & Zhao, R. (2019). An allocation scheme of students based on IPM under the nearby enrollment policy. 2019 10th International Conference on Information Technology in Medicine and Education (ITME), 260–264. https://doi.org/10.1109/ITME.2019.00066 [GS Search].
Lincolao-Venegas, I., & Rojas-Mora, J. (2024). Exploration Pitfalls in a CUDA Parallelized Simulated Annealing Algorithm to Solve the Centralized Student-School Assignment Problem. 2024 43rd International Conference of the Chilean Computer Science Society (SCCC), 1–8. https://doi.org/10.1109/SCCC63879.2024.10767637 [GS Search].
Machado, M. M. M., & Cardoso, S. L. (2025). O uso da Tecnologia em Sala de Aula com Alunos com TEA: uma Revisão Sistemática da Literatura. Revista Brasileira de Informática na Educação, 33, 583–604. https://doi.org/10.5753/rbie.2025.5356 [GS Search].
Mayerle, S. F., Rodrigues, H. F., Figueiredo, J. N., & Chiroli, D. M. D. G. (2022). Optimal student/school/class/teacher/classroom matching to support efficient public school system resource allocation. Socio-Economic Planning Sciences, 83, 101341. https://doi.org/10.1016/j.seps.2022.101341 [GS Search].
Neto, G. F., Miranda, P. B., Mello, R. F., & Nascimento, A. C. (2022). A Novel One-to-Many Matching Method for the Assignment Problem: An ENEM Case Study. Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 164–173. https://doi.org/10.5753/eniac.2022.227548 [GS Search].
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372. https://doi.org/10.1186/s13643-021-01626-4 [GS Search].
Prima, P., & Arymurthy, A. M. (2018). Optimization of school location-allocation using genetic algorithm. 2018 8th International Workshop on Computer Science and Engineering, WCSE 2018, 750–755. [GS Search] [Link].
Prima, P., & Arymurthy, A. M. (2019). Optimization of school location-allocation using Firefly Algorithm. Journal of Physics: Conference Series, 1235(1), 012002. https://doi.org/10.1088/1742-6596/1235/1/012002 [GS Search].
Robertson, S., Nguyen, T., & Salehi, N. (2021). Modeling assumptions clash with the real world: Transparency, equity, and community challenges for student assignment algorithms. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3411764.3445748 [GS Search].
Rodrigues, H. F., & Mayerle, S. F. (2014). Modelo capacitado para alocação de alunos em redes públicas de ensino. XLVI Simpósio Brasileiro de Pesquisa Operacional. [GS Search] [Link].
Rosistolato, R., Prado, A. P., Koslinski, M. C., Carvalho, J. T., & Moreira, A. M. (2016). Dinâmicas de matrícula em escolas públicas na cidade do Rio de Janeiro. Pro-Posições, 27, 237–262. https://doi.org/10.1590/1980-6248-2015-0108 [GS Search].
Verhalen, L. E. C., Castro, M. M. M., & Maciel, C. (2025). Percepções e Ferramentas sobre Recriação Digital de Educadores por meio de Inteligência Artificial: Uma Revisão Sistemática de Literatura. Revista Brasileira de Informática na Educação, 33, 565–582. https://doi.org/10.5753/rbie.2025.5279 [GS Search].
Vianna, C. R., Teles, C. A. M. S., Pontes, R. S. G., Belloze, K. T., Cirto, F. E., Brandão, D., Assis, L. S., & Carvalho, F. F. (2023). Heuristic for the Students Allocation in Brazilian Public Schools: a Case Study. 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), 1–7. https://doi.org/10.23919/CISTI58278.2023.10211339 [GS Search].
Wahid, E. A. L., Zainuddin, Z., & Ilham, A. A. (2024). Modification of K-Means Clustering Algorithm for Optimizing School Zoning System Using Big Data. 2024 7th International Conference on Information and Communications Technology (ICOIACT), 148–153. https://doi.org/10.1109/ICOIACT64819.2024.10913295 [GS Search].
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Anderson Pinheiro Cavalcanti, Samara Monteiro Xavier, Juliana Maria da Silva Venâncio, Péricles Barbosa Cunha de Miranda, André Câmara Alves do Nascimento, Rafael Ferreira Leite de Mello
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
