UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison

This paper introduces UniSchedApi, an API-based solution that revolutionizes optimized university resource scheduling. The primary focus of the research is the detailed evaluation of two automatic resource allocation methods: Tabu Search (TS) and Genetic Algorithm (GA). The paper thoroughly explores...

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Autores:
La Cruz, Alexandra
Herrera, Luis
Cortes, Jeisson
García-León, Andrés Alberto
Severeyn, Erika
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Universidad de Ibagué
Repositorio:
Repositorio Universidad de Ibagué
Idioma:
eng
OAI Identifier:
oai:repositorio.unibague.edu.co:20.500.12313/6043
Acceso en línea:
https://doi.org/10.32397/tesea.vol5.n2.633
https://hdl.handle.net/20.500.12313/6043
https://revistas.utb.edu.co/tesea/article/view/633
Palabra clave:
Recursos universitarios
Metodología universitarias - Comparación
Genetic Algorithms
Metaheuristic Algorithms
Optimization
Optimization algorithms
Scheduling problem
Rights
openAccess
License
© 2024 by the authors.
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dc.title.eng.fl_str_mv UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison
title UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison
spellingShingle UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison
Recursos universitarios
Metodología universitarias - Comparación
Genetic Algorithms
Metaheuristic Algorithms
Optimization
Optimization algorithms
Scheduling problem
title_short UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison
title_full UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison
title_fullStr UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison
title_full_unstemmed UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison
title_sort UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison
dc.creator.fl_str_mv La Cruz, Alexandra
Herrera, Luis
Cortes, Jeisson
García-León, Andrés Alberto
Severeyn, Erika
dc.contributor.author.none.fl_str_mv La Cruz, Alexandra
Herrera, Luis
Cortes, Jeisson
García-León, Andrés Alberto
Severeyn, Erika
dc.subject.armarc.none.fl_str_mv Recursos universitarios
Metodología universitarias - Comparación
topic Recursos universitarios
Metodología universitarias - Comparación
Genetic Algorithms
Metaheuristic Algorithms
Optimization
Optimization algorithms
Scheduling problem
dc.subject.proposal.eng.fl_str_mv Genetic Algorithms
Metaheuristic Algorithms
Optimization
Optimization algorithms
Scheduling problem
description This paper introduces UniSchedApi, an API-based solution that revolutionizes optimized university resource scheduling. The primary focus of the research is the detailed evaluation of two automatic resource allocation methods: Tabu Search (TS) and Genetic Algorithm (GA). The paper thoroughly explores how these methods address challenges associated with resource allocation in university environments, considering critical factors such as teacher availability, student time constraints, classroom features (including computers, projectors, TV’s, specialized laboratories, specialized equipment, etc.), among others. The evaluation is carried out meticulously, measuring the performance and memory resource usage of both algorithms, considering the comparison with the manual scheduling. The results reveal that the TS algorithm excels in terms of temporal efficiency and computational resource usage. Based on these findings, UniSchedApi implements GA and TS but uses TS as the default algorithm, ensuring more efficient and optimized management of academic resources. This research not only presents a practical solution with UniSchedApi but also provides a deep understanding of the methods for evaluating and selecting algorithms to address specific challenges in university resource allocation. These results lay the groundwork for future improvements in academic resource management.
publishDate 2024
dc.date.issued.none.fl_str_mv 2024-07-31
dc.date.accessioned.none.fl_str_mv 2025-11-27T15:40:18Z
dc.date.available.none.fl_str_mv 2025-11-27T15:40:18Z
dc.type.none.fl_str_mv Artículo de revista
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dc.identifier.citation.none.fl_str_mv La Cruz, A., Herrera, L., Cortes, J., García-León, A. A., & Severeyn, E. (2024). UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison. Transactions on Energy Systems and Engineering Applications, 5(2), 1–13. https://doi.org/10.32397/tesea.vol5.n2.633
dc.identifier.doi.none.fl_str_mv https://doi.org/10.32397/tesea.vol5.n2.633
dc.identifier.issn.none.fl_str_mv 27450120
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/6043
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identifier_str_mv La Cruz, A., Herrera, L., Cortes, J., García-León, A. A., & Severeyn, E. (2024). UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison. Transactions on Energy Systems and Engineering Applications, 5(2), 1–13. https://doi.org/10.32397/tesea.vol5.n2.633
27450120
url https://doi.org/10.32397/tesea.vol5.n2.633
https://hdl.handle.net/20.500.12313/6043
https://revistas.utb.edu.co/tesea/article/view/633
dc.language.iso.none.fl_str_mv eng
language eng
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dc.relation.citationvolume.none.fl_str_mv 5
dc.relation.ispartofjournal.none.fl_str_mv Transactions on Energy Systems and Engineering Applications
dc.relation.references.none.fl_str_mv A.R Mushi. Tabu search heuristic for university course timetabling problem.African Journal of Science and Technology,7(1), 2006.
H. Raoofpanah and V. Ghezavati. Extended hybrid tabu search and simulated annealing algorithm for location-inventorymodel with multiple products, multiple distribution centers and multiple capacity levels.Production Engineering Researchand Development, 13:649–663, 2019
X. Deng, Y. Zhang, B. Kang, J. Wu, X. Sun, and Y. Deng. An application of genetic algorithm for university coursetimetabling problem. InProceedings of the 23rd Chinese Control and Decision Conference (CCDC 2011), pages 2119–2122,2011
Rhydian Lewis. A survey of metaheuristic-based techniques for university timetabling problems.OR Spectrum, 30:167–190,01 2008.
Marieke Adriaen, Patrick De Causmaecker, and Piet Demeester. Tackling the university course timetabling problem withan aggregation approach. InProceedings of the 7th International Conference on the Practice and Theory of AutomatedTimetabling (PATAT 2006), pages 330–335, 2006
Ahmed A. Mahiba and Chitharanjan A. D. Durai. Genetic algorithm with search bank strategies for university coursetimetabling problem.Procedia Engineering, 38:253–263, 2012
Michael R. R. Lewis.Metaheuristics for University Course Timetabling. PhD thesis, Napier University, 2006.
M. Joudaki, M. Imani, and N. Mazhari. Using improved memetic algorithm and local search to solve university coursetimetabling problem (ucttp). Doroud, Iran, 2010. Islamic Azad University.
Robert Pellerin, Nathalie Perrier, and François Berthaut. A survey of hybrid metaheuristics for the resource-constrainedproject scheduling problem.European Journal of Operational Research, 280(2):395–416, 2020.
Wouter Kool, Herke van Hoof, and Max Welling. Attention, learn to solve routing problems! InInternational Conference onLearning Representations, 2019
P. Nandal, Ankit Satyawali, Dhananjay Sachdeva, and Abhinav Singh Tomar. Graph coloring based scheduling algorithm toautomatically generate college course timetable. In2021 11th International Conference on Cloud Computing, Data ScienceEngineering (Confluence), pages 210–214, 2021
Sally C. Brailsford, Chris N. Potts, and Barbara M. Smith. Constraint satisfaction problems: Algorithms and applications.European Journal of Operational Research, 119(3):557–581, 1999
Tadeusz Sawik.Scheduling in Supply Chains Using Mixed Integer Programming. Wiley, 2011.
L. Buriol, P.M. França, and P. Moscato. A new memetic algorithm for the asymmetric traveling salesman problem.Journalof Heuristics, 10:483–506, 2004
Marek Mika, Grzegorz Waligóra, and Jan W ̨eglarz. Tabu search for multi-mode resource-constrained project scheduling withschedule-dependent setup times.European Journal of Operational Research, 187(3):1238–1250, 2008
Cuneyt Aladag and Gulay Hocaoglu. A tabu search algorithm to solve a course timetabling problem.Hacettepe Journal ofMathematics and Statistics, pages 53–64, 2007
Juan Frausto-Solís, Francisco Alonso-Pecina, and Jaime Mora-Vargas. An efficient simulated annealing algorithm forfeasible solutions of course timetabling. InProceedings of the 10th European Conference on Evolutionary Computation inCombinatorial Optimization (EvoCOP 2008), pages 675–685, 2008
Juan Soria-Alcaraz, Gabriela Ochoa, Jerry Swan, Miguel Carpio, Héctor Puga, and Edmund Burke. Effective learninghyper-heuristics for the course timetabling problem.European Journal of Operational Research, pages 77–86, 2014
S. Castillo-Rivera, J. De Antón, R. del Olmo, J. Pajares, and A. López-Paredes. Genetic algorithms for the scheduling inadditive manufacturing.International Journal of Production Management and Engineering, 8(2):59–63, 2020.
Scheduling under Resource Constraints, pages 425–475. Springer Berlin Heidelberg, Berlin, Heidelberg, 2007
S.N. Jat and S. Yang. A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling.Journalof Scheduling, 14:617–637, 2011
Fred Glover and Manuel Laguna.Tabu Search, pages 3261–3362. Springer New York, New York, NY, 2013
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spelling La Cruz, Alexandra22803b00-0ef2-4c49-9bc4-ad26d39e15fc-1Herrera, Luis783a7ccc-7b26-4004-9bc4-6b510a880dc1-1Cortes, Jeisson6c7853e9-ae1d-4aa7-bcf8-b043f4eaa698-1García-León, Andrés Alberto3285d23e-12f4-4295-9c8c-93c8d8739484-1Severeyn, Erikac5e9527a-35c2-4407-b46b-1192d15b1c58-12025-11-27T15:40:18Z2025-11-27T15:40:18Z2024-07-31This paper introduces UniSchedApi, an API-based solution that revolutionizes optimized university resource scheduling. The primary focus of the research is the detailed evaluation of two automatic resource allocation methods: Tabu Search (TS) and Genetic Algorithm (GA). The paper thoroughly explores how these methods address challenges associated with resource allocation in university environments, considering critical factors such as teacher availability, student time constraints, classroom features (including computers, projectors, TV’s, specialized laboratories, specialized equipment, etc.), among others. The evaluation is carried out meticulously, measuring the performance and memory resource usage of both algorithms, considering the comparison with the manual scheduling. The results reveal that the TS algorithm excels in terms of temporal efficiency and computational resource usage. Based on these findings, UniSchedApi implements GA and TS but uses TS as the default algorithm, ensuring more efficient and optimized management of academic resources. This research not only presents a practical solution with UniSchedApi but also provides a deep understanding of the methods for evaluating and selecting algorithms to address specific challenges in university resource allocation. These results lay the groundwork for future improvements in academic resource management.application/pdfLa Cruz, A., Herrera, L., Cortes, J., García-León, A. A., & Severeyn, E. (2024). UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison. Transactions on Energy Systems and Engineering Applications, 5(2), 1–13. https://doi.org/10.32397/tesea.vol5.n2.633https://doi.org/10.32397/tesea.vol5.n2.63327450120https://hdl.handle.net/20.500.12313/6043https://revistas.utb.edu.co/tesea/article/view/633engUniversidad Tecnologica de BolivarColombia13215Transactions on Energy Systems and Engineering ApplicationsA.R Mushi. Tabu search heuristic for university course timetabling problem.African Journal of Science and Technology,7(1), 2006.H. Raoofpanah and V. Ghezavati. Extended hybrid tabu search and simulated annealing algorithm for location-inventorymodel with multiple products, multiple distribution centers and multiple capacity levels.Production Engineering Researchand Development, 13:649–663, 2019X. Deng, Y. Zhang, B. Kang, J. Wu, X. Sun, and Y. Deng. An application of genetic algorithm for university coursetimetabling problem. InProceedings of the 23rd Chinese Control and Decision Conference (CCDC 2011), pages 2119–2122,2011Rhydian Lewis. A survey of metaheuristic-based techniques for university timetabling problems.OR Spectrum, 30:167–190,01 2008.Marieke Adriaen, Patrick De Causmaecker, and Piet Demeester. Tackling the university course timetabling problem withan aggregation approach. InProceedings of the 7th International Conference on the Practice and Theory of AutomatedTimetabling (PATAT 2006), pages 330–335, 2006Ahmed A. Mahiba and Chitharanjan A. D. Durai. Genetic algorithm with search bank strategies for university coursetimetabling problem.Procedia Engineering, 38:253–263, 2012Michael R. R. Lewis.Metaheuristics for University Course Timetabling. PhD thesis, Napier University, 2006.M. Joudaki, M. Imani, and N. Mazhari. Using improved memetic algorithm and local search to solve university coursetimetabling problem (ucttp). Doroud, Iran, 2010. Islamic Azad University.Robert Pellerin, Nathalie Perrier, and François Berthaut. A survey of hybrid metaheuristics for the resource-constrainedproject scheduling problem.European Journal of Operational Research, 280(2):395–416, 2020.Wouter Kool, Herke van Hoof, and Max Welling. Attention, learn to solve routing problems! InInternational Conference onLearning Representations, 2019P. Nandal, Ankit Satyawali, Dhananjay Sachdeva, and Abhinav Singh Tomar. Graph coloring based scheduling algorithm toautomatically generate college course timetable. In2021 11th International Conference on Cloud Computing, Data ScienceEngineering (Confluence), pages 210–214, 2021Sally C. Brailsford, Chris N. Potts, and Barbara M. Smith. Constraint satisfaction problems: Algorithms and applications.European Journal of Operational Research, 119(3):557–581, 1999Tadeusz Sawik.Scheduling in Supply Chains Using Mixed Integer Programming. Wiley, 2011.L. Buriol, P.M. França, and P. Moscato. A new memetic algorithm for the asymmetric traveling salesman problem.Journalof Heuristics, 10:483–506, 2004Marek Mika, Grzegorz Waligóra, and Jan W ̨eglarz. Tabu search for multi-mode resource-constrained project scheduling withschedule-dependent setup times.European Journal of Operational Research, 187(3):1238–1250, 2008Cuneyt Aladag and Gulay Hocaoglu. A tabu search algorithm to solve a course timetabling problem.Hacettepe Journal ofMathematics and Statistics, pages 53–64, 2007Juan Frausto-Solís, Francisco Alonso-Pecina, and Jaime Mora-Vargas. An efficient simulated annealing algorithm forfeasible solutions of course timetabling. InProceedings of the 10th European Conference on Evolutionary Computation inCombinatorial Optimization (EvoCOP 2008), pages 675–685, 2008Juan Soria-Alcaraz, Gabriela Ochoa, Jerry Swan, Miguel Carpio, Héctor Puga, and Edmund Burke. Effective learninghyper-heuristics for the course timetabling problem.European Journal of Operational Research, pages 77–86, 2014S. Castillo-Rivera, J. De Antón, R. del Olmo, J. Pajares, and A. López-Paredes. Genetic algorithms for the scheduling inadditive manufacturing.International Journal of Production Management and Engineering, 8(2):59–63, 2020.Scheduling under Resource Constraints, pages 425–475. Springer Berlin Heidelberg, Berlin, Heidelberg, 2007S.N. Jat and S. Yang. A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling.Journalof Scheduling, 14:617–637, 2011Fred Glover and Manuel Laguna.Tabu Search, pages 3261–3362. Springer New York, New York, NY, 2013© 2024 by the authors.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/Recursos universitariosMetodología universitarias - ComparaciónGenetic AlgorithmsMetaheuristic AlgorithmsOptimizationOptimization algorithmsScheduling problemUniSchedApi: A comprehensive solution for university resource scheduling and methodology comparisonArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/544d3d80-12f5-438e-afb9-707ceed692c9/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf113653https://repositorio.unibague.edu.co/bitstreams/a4a08460-e3fc-4f96-b6ff-5e7d78d6102b/download365fa2bf301027ee24eb6325d2b04a90MD52TEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain2245https://repositorio.unibague.edu.co/bitstreams/3146ca6c-d4f0-4706-9f6b-ab0230276333/downloadb49840b95104148abe1263da8a80fbaeMD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg23267https://repositorio.unibague.edu.co/bitstreams/39227c19-fb3c-4e89-89f2-8d35350e534c/download53cec786590ef944f8316d1dfe33e3abMD5420.500.12313/6043oai:repositorio.unibague.edu.co:20.500.12313/60432025-11-28 03:02:42.876https://creativecommons.org/licenses/by/4.0/© 2024 by the authors.https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

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