Comparison of bioinspired algorithms applied to the timetabling problem in sport
The problems of timetabling tasks or events, in general, are subject to sets of restrictions such as: creation of work roles, operation of different teams, operation personnel, among others. These types of problems are classified as NP class [1]. In this study, special attention is paid to the sched...
- Autores:
-
Silva, Jesus
Cabrera, Danelys
Maco, José
Villón, Martín
Garcia Guliany, Jesus
RONCALLO PICHON, ALBERTO DE JESUS
Hernández Palma, Hugo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7791
- Acceso en línea:
- https://hdl.handle.net/11323/7791
https://doi.org/10.1016/j.procs.2020.03.100
https://repositorio.cuc.edu.co/
- Palabra clave:
- análisis de redes sociales
grafos
actores influyentes
interdisciplina
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Comparison of bioinspired algorithms applied to the timetabling problem in sport |
| title |
Comparison of bioinspired algorithms applied to the timetabling problem in sport |
| spellingShingle |
Comparison of bioinspired algorithms applied to the timetabling problem in sport análisis de redes sociales grafos actores influyentes interdisciplina |
| title_short |
Comparison of bioinspired algorithms applied to the timetabling problem in sport |
| title_full |
Comparison of bioinspired algorithms applied to the timetabling problem in sport |
| title_fullStr |
Comparison of bioinspired algorithms applied to the timetabling problem in sport |
| title_full_unstemmed |
Comparison of bioinspired algorithms applied to the timetabling problem in sport |
| title_sort |
Comparison of bioinspired algorithms applied to the timetabling problem in sport |
| dc.creator.fl_str_mv |
Silva, Jesus Cabrera, Danelys Maco, José Villón, Martín Garcia Guliany, Jesus RONCALLO PICHON, ALBERTO DE JESUS Hernández Palma, Hugo |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesus Cabrera, Danelys Maco, José Villón, Martín Garcia Guliany, Jesus RONCALLO PICHON, ALBERTO DE JESUS Hernández Palma, Hugo |
| dc.subject.spa.fl_str_mv |
análisis de redes sociales grafos actores influyentes interdisciplina |
| topic |
análisis de redes sociales grafos actores influyentes interdisciplina |
| description |
The problems of timetabling tasks or events, in general, are subject to sets of restrictions such as: creation of work roles, operation of different teams, operation personnel, among others. These types of problems are classified as NP class [1]. In this study, special attention is paid to the scheduling problem applied to sports clubs, for which different bioinspired algorithms are implemented and compared. |
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2020 |
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2020 |
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2021-01-28T20:01:30Z |
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2021-01-28T20:01:30Z |
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https://doi.org/10.1016/j.procs.2020.03.100 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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1 Jorge A.S., Martin C.J., Hugo T. Academic timetabling design using hyper- heuristics, Springer Berlin Heidelberg, Berlin, Heidelberg (2011; 2010), pp. 43-56 doi:10.1007/978-3- 642-15534-5_3 2 Obit, J.H., Ouelhadj, D., Landa-Silva, D., Vun, T.K., & Alfred, R.: Designing a multi- agent approach system for distributed course timetabling, pp. 103–108, doi:10.1109/HIS.2011.6122088 (2011) 3 Lewis, M.R.R.: Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University (2006) 4 Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., & Deng, Y.: An application of genetic al- gorithm for university course timetabling problem, pp. 2119–2122, doi:10.1109/CCDC.2011.5968555 (2011) 5 Mahiba A.A., Durai C.A.D. Genetic algorithm with search bank strategies for universi- ty course timetabling problem Procedia Engineering, 38 (2012), pp. 253-263 6 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham. 7 Nguyen, K., Lu, T., Le, T., & Tran, N.: Memetic algorithm for a university course timeta- bling problem. pp. 67-71, doi:10.1007/978-3-642-25899-2_10 (2011) 8 Asratian, A.S., de Werra, D.: A generalized class–teacher model for some timetabling problems, University of Technology, Department of Engineering Sciences and Mathemat- ics, Mathematical Science, & Mathematics, European Journal of Operational Research, pp. 531–542 (2002) doi:10.1016/S0377-2217(01)00342-3. 9 Soria-Alcaraz Jorge A., Martín C., Héctor P., Sotelo-Figueroa M.A. Comparison of metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem, Springer Berlin Heidel- berg, Berlin, Heidelberg (2013), pp. 289-302 doi:10.1007/978-3-642-33021-6_23 10 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018) 11 De Werra D. An introduction to timetabling European Journal of Operational Research, 19 (2) (1985), pp. 151-162 12 Talbi E. Metaheuristics: From design to implementation, Wiley, US (2009) 13 Goldberg D.E. Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Pub. Co., Read- ing, Mass (1989) 14 Yang X.-S. Nature-inspired metaheuristic algorithms, Luniver press (2010) 15 Abdoun O., Abouchabaka J.: A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem. International Journal of Computer Applications (2011) 16 Derrac, J., García, S.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence. Swarm and Evolutionary Computation (2011) 17 Azuaje F. Review of “Artificial immune systems: A new computational intelligence approach Journal Neural Networks, Elsevier (2003), p. 1229 18 Maulik U., Bandyopadhyay S. Genetic algorithm-based clustering technique Pattern Recognition, 33 (2000), pp. 1455-1465 19 Lü, Z., & Hao, J.: Adaptive tabu search for course timetabling. European Journal of Operational Research, pp. 235–244, doi:10.1016/j.ejor.2008.12.007 (2010) 20 Conant-Pablos, S.E., et al.: Pipelining Memetic algorithms, constraint satisfaction, and local search for course timetabling. In: MICAI Mexican International Conference on Artifi- cial Intelligence, vol. 1, pp 408–419 (2009) 21 Carpio-Valadez J.M. Integral Model for optimal assignation of academic tasks Encuentro de investigacion en ingenieria electrica, ENVIE, Zacatecas (2006), pp. 78-83 22 Viloria A., Lezama O.B.P. Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 1201-1206 |
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Silva, Jesus659ae35f3326439474c6cca46ee77cb0Cabrera, Danelys1f9f790aa17165bd0e99ba6d950da3eeMaco, José3ae5a9f0859c736faaf0c56a1aaec025Villón, Martínf63f5e5ca7d5f396ac1e41161469c7c0Garcia Guliany, Jesus15cec4fe6253f5582063ebc2c62c8298RONCALLO PICHON, ALBERTO DE JESUS36e646e74fa91ddf630620e97063fd36Hernández Palma, Hugo5be75fc527c47a185f94ec4869f8c5d22021-01-28T20:01:30Z2021-01-28T20:01:30Z2020https://hdl.handle.net/11323/7791https://doi.org/10.1016/j.procs.2020.03.100Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The problems of timetabling tasks or events, in general, are subject to sets of restrictions such as: creation of work roles, operation of different teams, operation personnel, among others. These types of problems are classified as NP class [1]. In this study, special attention is paid to the scheduling problem applied to sports clubs, for which different bioinspired algorithms are implemented and compared.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S187705092030538X#!análisis de redes socialesgrafosactores influyentesinterdisciplinaComparison of bioinspired algorithms applied to the timetabling problem in sportArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 Jorge A.S., Martin C.J., Hugo T. Academic timetabling design using hyper- heuristics, Springer Berlin Heidelberg, Berlin, Heidelberg (2011; 2010), pp. 43-56 doi:10.1007/978-3- 642-15534-5_32 Obit, J.H., Ouelhadj, D., Landa-Silva, D., Vun, T.K., & Alfred, R.: Designing a multi- agent approach system for distributed course timetabling, pp. 103–108, doi:10.1109/HIS.2011.6122088 (2011)3 Lewis, M.R.R.: Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University (2006)4 Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., & Deng, Y.: An application of genetic al- gorithm for university course timetabling problem, pp. 2119–2122, doi:10.1109/CCDC.2011.5968555 (2011)5 Mahiba A.A., Durai C.A.D. Genetic algorithm with search bank strategies for universi- ty course timetabling problem Procedia Engineering, 38 (2012), pp. 253-2636 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.7 Nguyen, K., Lu, T., Le, T., & Tran, N.: Memetic algorithm for a university course timeta- bling problem. pp. 67-71, doi:10.1007/978-3-642-25899-2_10 (2011)8 Asratian, A.S., de Werra, D.: A generalized class–teacher model for some timetabling problems, University of Technology, Department of Engineering Sciences and Mathemat- ics, Mathematical Science, & Mathematics, European Journal of Operational Research, pp. 531–542 (2002) doi:10.1016/S0377-2217(01)00342-3.9 Soria-Alcaraz Jorge A., Martín C., Héctor P., Sotelo-Figueroa M.A. Comparison of metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem, Springer Berlin Heidel- berg, Berlin, Heidelberg (2013), pp. 289-302 doi:10.1007/978-3-642-33021-6_2310 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)11 De Werra D. An introduction to timetabling European Journal of Operational Research, 19 (2) (1985), pp. 151-16212 Talbi E. Metaheuristics: From design to implementation, Wiley, US (2009)13 Goldberg D.E. Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Pub. Co., Read- ing, Mass (1989)14 Yang X.-S. Nature-inspired metaheuristic algorithms, Luniver press (2010)15 Abdoun O., Abouchabaka J.: A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem. International Journal of Computer Applications (2011)16 Derrac, J., García, S.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence. Swarm and Evolutionary Computation (2011)17 Azuaje F. Review of “Artificial immune systems: A new computational intelligence approach Journal Neural Networks, Elsevier (2003), p. 122918 Maulik U., Bandyopadhyay S. Genetic algorithm-based clustering technique Pattern Recognition, 33 (2000), pp. 1455-146519 Lü, Z., & Hao, J.: Adaptive tabu search for course timetabling. European Journal of Operational Research, pp. 235–244, doi:10.1016/j.ejor.2008.12.007 (2010)20 Conant-Pablos, S.E., et al.: Pipelining Memetic algorithms, constraint satisfaction, and local search for course timetabling. In: MICAI Mexican International Conference on Artifi- cial Intelligence, vol. 1, pp 408–419 (2009)21 Carpio-Valadez J.M. Integral Model for optimal assignation of academic tasks Encuentro de investigacion en ingenieria electrica, ENVIE, Zacatecas (2006), pp. 78-8322 Viloria A., Lezama O.B.P. Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 1201-1206ORIGINALComparison of bioinspired algorithms applied to the timetabling problem in sport.pdfComparison of bioinspired algorithms applied to the timetabling problem in sport.pdfapplication/pdf99365https://repositorio.cuc.edu.co/bitstream/11323/7791/1/Comparison%20of%20bioinspired%20algorithms%20applied%20to%20the%20timetabling%20problem%20in%20sport.pdfdbc5aeda135a3700d0732ae1e5e9bde4MD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstream/11323/7791/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstream/11323/7791/3/license.txte30e9215131d99561d40d6b0abbe9badMD53open accessTHUMBNAILComparison of bioinspired algorithms applied to the timetabling problem in 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