Optimización de rutas de transporte estudiantil en Bogotá: un enfoque basado en análisis espaciotemporal y programación matemática
| dc.contributor.advisor | Romero Gélvez, Jorge Iván | |
| dc.creator | Hernández Zambrano, Leidy Tatiana | |
| dc.date.accessioned | 2025-12-19T18:11:16Z | |
| dc.date.created | 0025-01-30 | |
| dc.description.abstract | El transporte estudiantil representa un desafío logístico significativo en áreas urbanas densamente pobladas como Bogotá, Colombia. Este estudio presenta un modelo integral de optimización de rutas que combina análisis espaciotemporal de la demanda con técnicas de programación matemática. Mediante la generación de datos sintéticos representativos de patrones reales de movilidad estudiantil, se identificaron cuatro rutas principales (Norte-Sur, Occidente-Oriente, Sur-Norte y Oriente-Occidente) que cubren las zonas de mayor demanda de la ciudad. Se emplearon mapas de calor para visualizar la distribución espaciotemporal de puntos de recogida, revelando concentraciones significativas durante las horas pico (7:00-9:00 y 17:00-19:00). El modelo de optimización, implementado mediante Pyomo, considera restricciones de capacidad vehicular, tiempos de viaje variables según la hora del día y demanda horaria. Los resultados demuestran una reducción potencial del 23% en tiempos de recorrido y un incremento del 15% en la eficiencia de ocupación vehicular comparado con rutas convencionales. Este enfoque metodológico proporciona un marco replicable para la planificación de sistemas de transporte estudiantil en contextos urbanos similares. | |
| dc.description.abstractenglish | Student transportation presents a significant logistical challenge in densely populated urban areas like Bogotá, Colombia. This study presents a comprehensive route optimization model that combines spatiotemporal demand analysis with mathematical programming techniques. By generating synthetic data representative of real student mobility patterns, four main routes (North-South, West-East, South-North, and East-West) were identified, covering the city's highest-demand areas. Heat maps were used to visualize the spatiotemporal distribution of pick-up points, revealing significant concentrations during peak hours (7:00-9:00 and 17:00-19:00). The optimization model, implemented using Pyomo, considers vehicle capacity constraints, travel times that vary according to the time of day, and hourly demand. The results demonstrate a potential 23% reduction in travel times and a 15% increase in vehicle occupancy efficiency compared to conventional routes. This methodological approach provides a replicable framework for planning student transport systems in similar urban contexts. | |
| dc.format.extent | 6 páginas | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12010/38694 | |
| dc.language.iso | es | |
| dc.relation.references | [1] J. F. Cordeau, G. Laporte, and A. Mercier, “A unified tabu search heuristic for vehicle routing problems with time windows,” Journal of the Operational Research Society, vol. 52, no. 8, pp. 928–936, 2001. | |
| dc.relation.references | [2] G. Desaulniers, O. B. Madsen, and S. Ropke, “The vehicle routing problem with time windows,” in Vehicle Routing: Problems, Methods, and Applications, 2014, pp. 119–159. | |
| dc.relation.references | [3] W. E. Hart, J. P. Watson, and D. L. Woodruff, “Pyomo: modeling and solving mathematical programs in Python,” Mathematical Programming Computation, vol. 3, no. 3, pp. 219–260, 2011. | |
| dc.relation.references | [4] B. I. Kim, S. Kim, and J. Park, “A school bus routing problem with bus stop selection problem,” Transportation Research Part C: Emerging Technologies, vol. 29, pp. 94–109, 2012. | |
| dc.relation.references | [5] J. Park and B. I. Kim, “The school bus routing problem: A review,” European Journal of Operational Research, vol. 202, no. 2, pp. 311– 319, 2010. | |
| dc.relation.references | [6] J. Riera-Ledesma and J. J. Salazar-Gonz´alez, “Solving school bus routing using the multiple vehicle traveling purchaser problem: A branchand- cut approach,” Computers & Operations Research, vol. 39, no. 2, pp. 391–404, 2012. | |
| dc.relation.references | [7] P. Schittekat, J. Kinable, K. S¨orensen, M. Sevaux, F. Spieksma, and J. Springael, “A metaheuristic for the school bus routing problem with bus stop selection,” European Journal of Operational Research, vol. 229, no. 2, pp. 518–528, 2013. | |
| dc.relation.references | [8] P. Toth and D. Vigo, Eds., Vehicle Routing: Problems, Methods, and Applications. Society for Industrial and Applied Mathematics, 2014. | |
| dc.subject | Optimización de rutas | |
| dc.subject | Transporte estudiantil | |
| dc.subject | Análisis espaciotemporal | |
| dc.subject | Programación matemática | |
| dc.subject | Movilidad urbana | |
| dc.subject | Bogotá | |
| dc.subject.keyword | Student transportation | |
| dc.subject.keyword | Spatiotemporal analysis | |
| dc.subject.keyword | Urban mobility | |
| dc.subject.keyword | Bogotá | |
| dc.subject.lemb | Transporte escolar - Planificación de rutas | |
| dc.subject.lemb | Transporte urbano - Modelos matemáticos | |
| dc.subject.lemb | Sistemas de transporte - Eficiencia operativa | |
| dc.title | Optimización de rutas de transporte estudiantil en Bogotá: un enfoque basado en análisis espaciotemporal y programación matemática | |
| dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 |
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