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Modelo para la programación horaria de un programa académico en una Institución de Educación Superior (IES)
dc.contributor.advisor | Guerrero Rueda, William Javier | |
dc.contributor.advisor | Yepes Borrero, Juan Camilo | |
dc.contributor.author | Moreno Cortés, Jessica Ximena | |
dc.date.accessioned | 2024-02-29T14:00:33Z | |
dc.date.available | 2024-02-29T14:00:33Z | |
dc.date.issued | 2023-10-13 | |
dc.identifier.uri | http://hdl.handle.net/10818/59337 | |
dc.description | 69 paginas | es_CO |
dc.description.abstract | La programación de horarios de clases académicas en una Institución de Educación Superior (IES) es una actividad que se realiza antes de iniciar cada período académico establecido para un programa de educación superior. Donde el horizonte de planeación puede ser semestral, trimestral, cuatrimestral o mensual dependiendo de periodicidad establecida para un programa académico y en este se establecen los horarios para la cantidad de periodos académicos establecidos en el plan de estudios y los espacios académicos. Actualmente, puede ser una de las actividades donde se emplean tiempos prolongados, se requiere personal especializado para su planeación y ejecución, con el fin de dar cumplimiento a los requisitos, lineamientos, reglamentaciones, limitaciones de infraestructura o aspectos relevantes determinados por el departamento o departamentos que intervienen en la programación académica para este programa, de acuerdo con el plan de estudios, prerrequisitos y la pertinencia de los aspectos curriculares, escenarios de aprendizaje y la organización de las actividades académicas. | es_CO |
dc.format | application/pdf | es_CO |
dc.language.iso | spa | es_CO |
dc.publisher | Universidad de La Sabana | es_CO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject.other | Horarios universitarios | |
dc.subject.other | Restricciones | |
dc.title | Modelo para la programación horaria de un programa académico en una Institución de Educación Superior (IES) | es_CO |
dc.type | master thesis | es_CO |
dc.type.hasVersion | publishedVersion | es_CO |
dc.rights.accessRights | restrictedAccess | es_CO |
dc.subject.armarc | Educación superior | |
dc.subject.armarc | Planificación estratégica | |
dcterms.references | Abduljabbar, I. A., & Abdullah, S. M. (2022). An evolutionary algorithm for solving academic courses timetable scheduling problem. Baghdad Science Journal, 19(2), 399–408. https://doi.org/10.21123/BSJ.2022.19.2.0399 | |
dcterms.references | Alansari, M. M. (2022). Optimized Automatic Course Timetabling Service Architecture for Integration with Vendor Management Systems. International Journal of Advanced Computer Science and Applications, 13(10), 878–890. https://doi.org/10.14569/IJACSA.2022.01310104 | |
dcterms.references | Alkallak, I. N., Shaban, R. Z., & Alnema, Y. H. S. (2022). Intelligent university timetable scheduling system using sudoku grid with magic square. Bulletin of Electrical Engineering and Informatics, 11(3), 1526– 1534. https://doi.org/10.11591/eei.v11i3.3694 | |
dcterms.references | Akkan, C., Gülcü, A., & Kuş, Z. (2022). Bi-criteria simulated annealing for the curriculum-based course timetabling problem with robustness approximation. Journal of Scheduling, 25(4), 477–501. https://doi.org/10.1007/s10951-022-00722-0 | |
dcterms.references | Almonteros, J. R., Pacot, M. P. B., & Pitogo, V. A. (2022). Automation of Curriculum-based Student-Subject Encoding: A Web Application. ACM International Conference Proceeding Series, 328–333. https://doi.org/10.1145/3579895.3579944 | |
dcterms.references | Arbaoui, T., Boufflet, J. P., & Moukrim, A. (2019). Lower bounds and compact mathematical formulations for spacing soft constraints for university examination timetabling problems. Computers and Operations Research, 106, 133–142. https://doi.org/10.1016/j.cor.2019.02.013 | |
dcterms.references | Assi, M., Halawi, B., & Haraty, R. A. (2018). Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University 55 Timetable Problem. Procedia Computer Science, 126, 899–906. https://doi.org/10.1016/j.procS.2018.08.024 | |
dcterms.references | Bababeik, M., Zerguini, S., Farjad-Amin, M., Khademi, N., & Bagheri, M. (2019). Developing a train timetable according to track maintenance plans: A stochastic optimization of buffer time schedules. Transportation Research Procedia, 37 (September 2018), 27–34. https://doi.org/10.1016/j.trpro.2018.12.162 | |
dcterms.references | Barnhart, C., Bertsimas, D., Delarue, A., & Yan, J. (2022). Course Scheduling Under Sudden Scarcity: Applications to Pandemic Planning. Manufacturing and Service Operations Management, 24(2), 727–745. https://doi.org/10.1287/msom.2021.0996 | |
dcterms.references | Behrenk, A. B., Güçlükol Ergin, S., & Toy, A. Ö. (2023). Course Scheduling Problem and Real-Life Implementation. 749–758. https://doi.org/10.1007/978-3-031-24457-5_59 | |
dcterms.references | Chen, D., Li, S., Li, J., Ni, S., & Liu, X. (2019). Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving. Journal of Advanced Transportation, 2019. https://doi.org/10.1155/2019/4213095 | |
dcterms.references | Çimen, M., Belbağ, S., Soysal, M., & Sel, Ç. (2022). INVIGILATORS ASSIGNMENT IN PRACTICAL EXAMINATION TIMETABLING PROBLEMS. International Journal of Industrial Engineering : Theory Applications and Practice, 29(3), 351–371. https://doi.org/10.23055/ijietap.2022.29.3.6943 | |
dcterms.references | Diveev, A. I., Bobr, O. V., Kazaryan, D. E., & Hussein, O. (2019). Some methods of solving the NP-difficult problem of optimal schedule for the university. Procedia Computer Science, 150, 410–415. https://doi.org/10.1016/j.procs.2019.02.071 | |
dcterms.references | Dimitsas, A., Nastos, V., Gogos, C., & Valouxis, C. (2022). An exact based approach for the Post Enrollment Course Timetabling Problem. ACM International Conference Proceeding Series, 77–82. https://doi.org/10.1145/3575879.3575970 | |
dcterms.references | Dobrynin, A. S., Kulakov, S. M., & Taraborina, E. N. (2018). About the algorithm for construction of coordinated university timetables. IOP Conference Series: Materials Science and Engineering, 351(1), 5–9. https://doi.org/10.1088/1757-899X/354/1/012007 | |
dcterms.references | Dunke, F., & Nickel, S. (2023). A matheuristic for customized multilevel multi-criteria university timetabling. Annals of Operations Research, 10479. https://doi.org/10.1007/s10479-023-05325-2 | |
dcterms.references | Drábek, M. (2018). Irregularities in Czech integrated periodic timetable. MATEC Web of Conferences, 235(3), 10–13. https://doi.org/10.1051/matecconf/201823500012 | |
dcterms.references | Gestrelius, S., Aronsson, M., & Peterson, A. (2017). A MILP-based heuristic for a commercial train timetabling problem. Transportation Research Procedia, 27, 569–576. https://doi.org/10.1016/j.trpro.2017.12.118 | |
dcterms.references | Ghaffar, A., Sattar, M. U., Munir, M., & Qureshi, Z. (2022). Multiobjective fuzzy-based adaptive memetic algorithm with hyper-heuristics to solve university course timetabling problem. EAI Endorsed Transactions on Scalable Information Systems, 9(4). https://doi.org/10.4108/eai.16-12- 2021.172435 | |
dcterms.references | Güler, M. G., Geçici, E., Köroğlu, T., & Becit, E. (2021). A web-based decision support system for examination timetabling. Expert Systems with Applications, 183. https://doi.org/10.1016/j.eswa.2021.115363 | |
dcterms.references | Gülcü, A., & Akkan, C. (2020). Robust university course timetabling problem subject to single and multiple disruptions. En European Journal of 57 Operational Research (Vol. 283, Número 2, pp. 630–646). https://doi.org/10.1016/j.ejor.2019.11.024 | |
dcterms.references | Hussein, S. I., Akhand, M. A. H., Shuvo, M. I. R., Siddique, N., & Adeli, H. (2019). Optimization of university course scheduling problem using particle swarm optimization with selective search. Expert systems with applications, 127, 9-24. | |
dcterms.references | Holm, D. S., Mikkelsen, R. Ø., Sørensen, M., & Stidsen, T. J. R. (2022). A graph-based MIP formulation of the International Timetabling Competition 2019. Journal of Scheduling, 25(4), 405–428. https://doi.org/10.1007/s10951-022-00724-y | |
dcterms.references | Isobe, Y., Hatsugai, H., Tanaka, A., Oiwa, Y., Ambe, T., Okada, A., Kitamura, S., Fukuta, Y., & Kunifuji, T. (2019). Automatic generation of train timetables from mesoscopic railway models by SMT-solver. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2, 325–335. https://doi.org/10.1587/transfun.E102.A.325 | |
dcterms.references | Jiang, F., Yu, D. Ben, & Ni, S. Q. (2017). An objective train timetabling quality evaluation method. Mathematical Problems in Engineering, 2017. https://doi.org/10.1155/2017/3047963 | |
dcterms.references | Joyce, J., Gitomer, D. H., & Iaconangelo, C. J. (2018). Classroom assignments as measures of teaching quality. Learning and Instruction, 54, 48–61. https://doi.org/10.1016/j.learninstruc.2017.08.001 | |
dcterms.references | Lan, Z., He, S., Song, R., & Hao, S. (2019). Optimizing Vehicle Scheduling Based on Variable Timetable by Benders-and-Price Approach. Journal of Advanced Transportation, 2019. https://doi.org/10.1155/2019/2781590 | |
dcterms.references | Lemos, A., Melo, F. S., Monteiro, P. T., & Lynce, I. (2019). Room usage optimization in timetabling: A case study at Universidade de Lisboa. Operations Research Perspectives, 6(July 2018), 100092. https://doi.org/10.1016/j.orp.2018.100092 | |
dcterms.references | Lemos, A., Monteiro, P. T., & Lynce, I. (2022). Introducing UniCorT: an iterative university course timetabling tool with MaxSAT. Journal of Scheduling, 25(4), 371–390. https://doi.org/10.1007/s10951-021-00695-6 | |
dcterms.references | Li, W., Peng, Q., Wen, C., & Xu, X. (2019). Comprehensive Optimization of a Metro Timetable Considering Passenger Waiting Time and Energy Efficiency. IEEE Access, 7, 160144–160167. https://doi.org/10.1109/ACCESS.2019.2950814 | |
dcterms.references | Liu, Y., Ming, H., Luo, X., Hu, L., & Sun, Y. (2022). Research on multiobjective course scheduling optimization of college teaching building under intermittent heating mode. Xi’an Jianzhu Keji Daxue Xuebao/Journal of Xi’an University of Architecture and Technology, 54(5), 710–717. https://doi.org/10.15986/j.1006-7930.2022.05.009 | |
dcterms.references | Liu, Y., Ming, H., Luo, X., Hu, L., & Sun, Y. (2023). Timetabling optimization of classrooms and self-study rooms in university teaching buildings based on the building controls virtual test bed platform considering energy efficiency. Building Simulation, 16(2), 263–277. https://doi.org/10.1007/s12273-022-0938-4 | |
dcterms.references | Lindahl, M., Mason, A. J., Stidsen, T., & Sørensen, M. (2018). A strategic view of University timetabling. European Journal of Operational Research, 266(1), 35–45. https://doi.org/10.1016/j.ejor.2017.09.022 | |
dcterms.references | Liu, T., & Ceder, A. (Avi). (2018). Integrated public transport timetable synchronization and vehicle scheduling with demand assignment: A biobjective bi-level model using deficit function approach. Transportation Research Part B: Methodological, 117, 935–955. https://doi.org/10.1016/j.trb.2017.08.024 | |
dcterms.references | Meng, L., Muneeb Abid, M., Jiang, X., Khattak, A., & Babar Khan, M. (2019). Increasing Robustness by Reallocating the Margins in the Timetable. Journal of Advanced Transportation, 2019. https://doi.org/10.1155/2019/1382394 | |
dcterms.references | Mikkelsen, R. Ø., & Holm, D. S. (2022). A parallelized matheuristic for the International Timetabling Competition 2019. Journal of Scheduling, 25(4), 429–452. https://doi.org/10.1007/s10951-022-00728-8 | |
dcterms.references | Mohd Zaulir, Z., Aziz, N. L. A., & Aizam, N. A. H. (2022). A General Mathematical Model for University Courses Timetabling: Implementation to a Public University in Malaysia. Malaysian Journal of Fundamental and Applied Sciences, 18(1), 82–94. https://doi.org/10.11113/MJFAS.V18N1.2408 | |
dcterms.references | Muklason, A., Irianti, R. G., & Marom, A. (2019). Automated Course Timetabling Optimization Using Tabu-Variable Neighborhood Search Based Hyper-Heuristic Algorithm. Procedia Computer Science, 161, 656– 664. https://doi.org/10.1016/j.procs.2019.11.169 | |
dcterms.references | Muklason, A., Syahrani, G. B., & Marom, A. (2019). Great Deluge Based Hyper-heuristics for Solving Real-world University Examination Timetabling Problem: New Data set and Approach. Procedia Computer Science, 161, 647–655. https://doi.org/10.1016/j.procs.2019.11.168 | |
dcterms.references | Nand, R., Sharma, B., & Chaudhary, K. (2022). An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling. PeerJ Computer Science, 8. https://doi.org/10.7717/peerj-cs.1068 | |
dcterms.references | Oktavia, M. (2020). Sobre el algoritmo para la construcción de horarios universitarios coordinados Sobre el algoritmo para la construcción de horarios universitarios coordinados F ; 5–9. | |
dcterms.references | Palmqvist, C. W., Olsson, N. O. E., & Winslott Hiselius, L. (2018). The Planners’ Perspective on Train Timetable Errors in Sweden. Journal of Advanced Transportation, 2018. https://doi.org/10.1155/2018/8502819 | |
dcterms.references | Rappos, E., Thiémard, E., Robert, S., & Hêche, J.-F. (2022). A mixedinteger programming approach for solving university course timetabling problems. Journal of Scheduling, 25(4), 391–404. https://doi.org/10.1007/s10951-021-00715-5 | |
dcterms.references | Shen, Y., Ren, G., & Liu, Y. (2018). Timetable design for minimizing passenger travel time and congestion for a single metro line. Promet - Traffic - Traffico, 30(1), 21–33. https://doi.org/10.7307/ptt.v30i1.2281 | |
dcterms.references | Song, T., Liu, S., Tang, X., Peng, X., & Chen, M. (2018). An iterated local search algorithm for the University Course Timetabling Problem. Applied Soft Computing Journal, 68, 597–608. https://doi.org/10.1016/j.asoc.2018.04.034 | |
dcterms.references | Song, T., Chen, M., Xu, Y., Wang, D., Song, X., & Tang, X. (2021). Competition-guided multi-neighborhood local search algorithm for the university course timetabling problem. Applied Soft Computing, 110. https://doi.org/10.1016/j.asoc.2021.107624 | |
dcterms.references | Stucchi, L., Falckenheiner, S., Hermoza, A., & Casafranca, F. (2014). Optimización de un problema de asignación generalizada a partir de un algoritmo de colonia de hormigas que incorpora un mecanismo de difusión. | |
dcterms.references | Thepphakorn, T., & Pongcharoen, P. (2023). Modified and hybridised bi-objective firefly algorithms for university course scheduling. Soft Computing, 7810. https://doi.org/10.1007/s00500-022-07810-5 | |
dcterms.references | Xu, W., Tan, Y., Sharma, B., & Wang, Z. (2019). Cyclic Timetable Scheduling Problem on High-speed Railway Line. Periodica Polytechnica Transportation Engineering, 48(1), 31–38. https://doi.org/10.3311/PPtr.12312 | |
dcterms.references | Yasari, P., Ranjbar, M., Jamili, N., & Shaelaie, M. H. (2019a). A twostage stochastic programming approach for a multi-objective course timetabling problem with courses cancelation risk. Computers and Industrial Engineering, 130(February), 650–660. https://doi.org/10.1016/j.cie.2019.02.050 | |
dcterms.references | Yasari, P., Ranjbar, M., Jamili, N., & Shaelaie, M. H. (2019b). A twostage stochastic programming approach for a multi-objective course timetabling problem with courses cancelation risk. Computers and Industrial Engineering, 130, 650–660. https://doi.org/10.1016/j.cie.2019.02.050 | |
dcterms.references | Zhang, J., Lam, W. H. K., & Chen, B. Y. (2016). On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows. European Journal of Operational Research, 249(1), 144–154. https://doi.org/10.1016/j.ejor.2015.08.050 | |
thesis.degree.discipline | Facultad de Ingeniería | es_CO |
thesis.degree.level | Maestría en Diseño y Gestión de Procesos | es_CO |
thesis.degree.name | Magíster en Diseño y Gestión de Procesos | es_CO |