Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach

Autores
Casco, María Cecilia; López Pires, Fabio; Barán, Benjamín; Martínez, Eustaquio A.
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This research addresses the problem of Student Allocation to Educational Establishments (SAEE) in a many-objetive optimization context. This problem affects the logistics of educational systems, as it is influenced by parameters such as availability, distance, infrastructure, among others. A first mathematical formulation of the SAEE problem is proposed with five objective functions for the optimization of: (1) the difference between the number of students assigned and the optimal number of students per class, (2) the average distance between the student’s home and the institution, (3) the use of institutions with better infrastructure, (4) the maximum distance, and (5) the variance of the number of students assigned per class. To solve the proposed formulation, a Multi-Objective Evolutionary Algorithm (MOEA) based on NSGA-II is proposed. To validate this proposal, data provided by the Ministry of Education and Science of Paraguay (MEC) corresponding to Ciudad del Este - Alto Paran´a, from first grade to third grade, of 90 schools and 15,763 students were considered. Experimental results show significant improvements in the variance of the number of students assigned per class (by 22 %), which implies a more balanced distribution of the student population in the classrooms.
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
student allocation
many-objective optimization
evolutionary computation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/176331
Acceder
id SEDICI_1a6963986cf9c3c4422d960fac1eccdf
oai_identifier_str oai:sedici.unlp.edu.ar:10915/176331
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Optimizing Student Assignment to Educational Establishments: A Many-Objective ApproachCasco, María CeciliaLópez Pires, FabioBarán, BenjamínMartínez, Eustaquio A.Ciencias Informáticasstudent allocationmany-objective optimizationevolutionary computationThis research addresses the problem of Student Allocation to Educational Establishments (SAEE) in a many-objetive optimization context. This problem affects the logistics of educational systems, as it is influenced by parameters such as availability, distance, infrastructure, among others. A first mathematical formulation of the SAEE problem is proposed with five objective functions for the optimization of: (1) the difference between the number of students assigned and the optimal number of students per class, (2) the average distance between the student’s home and the institution, (3) the use of institutions with better infrastructure, (4) the maximum distance, and (5) the variance of the number of students assigned per class. To solve the proposed formulation, a Multi-Objective Evolutionary Algorithm (MOEA) based on NSGA-II is proposed. To validate this proposal, data provided by the Ministry of Education and Science of Paraguay (MEC) corresponding to Ciudad del Este - Alto Paran´a, from first grade to third grade, of 90 schools and 15,763 students were considered. Experimental results show significant improvements in the variance of the number of students assigned per class (by 22 %), which implies a more balanced distribution of the student population in the classrooms.Red de Universidades con Carreras en Informática2024-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf390-400http://sedici.unlp.edu.ar/handle/10915/176331enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2428-5info:eu-repo/semantics/reference/hdl/10915/172755info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:47:28Zoai:sedici.unlp.edu.ar:10915/176331Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:47:28.846SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach
title Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach
spellingShingle Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach
Casco, María Cecilia
Ciencias Informáticas
student allocation
many-objective optimization
evolutionary computation
title_short Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach
title_full Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach
title_fullStr Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach
title_full_unstemmed Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach
title_sort Optimizing Student Assignment to Educational Establishments: A Many-Objective Approach
dc.creator.none.fl_str_mv Casco, María Cecilia
López Pires, Fabio
Barán, Benjamín
Martínez, Eustaquio A.
author Casco, María Cecilia
author_facet Casco, María Cecilia
López Pires, Fabio
Barán, Benjamín
Martínez, Eustaquio A.
author_role author
author2 López Pires, Fabio
Barán, Benjamín
Martínez, Eustaquio A.
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
student allocation
many-objective optimization
evolutionary computation
topic Ciencias Informáticas
student allocation
many-objective optimization
evolutionary computation
dc.description.none.fl_txt_mv This research addresses the problem of Student Allocation to Educational Establishments (SAEE) in a many-objetive optimization context. This problem affects the logistics of educational systems, as it is influenced by parameters such as availability, distance, infrastructure, among others. A first mathematical formulation of the SAEE problem is proposed with five objective functions for the optimization of: (1) the difference between the number of students assigned and the optimal number of students per class, (2) the average distance between the student’s home and the institution, (3) the use of institutions with better infrastructure, (4) the maximum distance, and (5) the variance of the number of students assigned per class. To solve the proposed formulation, a Multi-Objective Evolutionary Algorithm (MOEA) based on NSGA-II is proposed. To validate this proposal, data provided by the Ministry of Education and Science of Paraguay (MEC) corresponding to Ciudad del Este - Alto Paran´a, from first grade to third grade, of 90 schools and 15,763 students were considered. Experimental results show significant improvements in the variance of the number of students assigned per class (by 22 %), which implies a more balanced distribution of the student population in the classrooms.
Red de Universidades con Carreras en Informática
description This research addresses the problem of Student Allocation to Educational Establishments (SAEE) in a many-objetive optimization context. This problem affects the logistics of educational systems, as it is influenced by parameters such as availability, distance, infrastructure, among others. A first mathematical formulation of the SAEE problem is proposed with five objective functions for the optimization of: (1) the difference between the number of students assigned and the optimal number of students per class, (2) the average distance between the student’s home and the institution, (3) the use of institutions with better infrastructure, (4) the maximum distance, and (5) the variance of the number of students assigned per class. To solve the proposed formulation, a Multi-Objective Evolutionary Algorithm (MOEA) based on NSGA-II is proposed. To validate this proposal, data provided by the Ministry of Education and Science of Paraguay (MEC) corresponding to Ciudad del Este - Alto Paran´a, from first grade to third grade, of 90 schools and 15,763 students were considered. Experimental results show significant improvements in the variance of the number of students assigned per class (by 22 %), which implies a more balanced distribution of the student population in the classrooms.
publishDate 2024
dc.date.none.fl_str_mv 2024-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/176331
url http://sedici.unlp.edu.ar/handle/10915/176331
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-2428-5
info:eu-repo/semantics/reference/hdl/10915/172755
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
390-400
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844616338299944960
score 13.070432

Publicaciones similares