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Simulación del comportamiento del inventario frente a la reducción de lead time mediante estrategias de cargue y descargue en horarios no convencionales :Un caso de estudio en Bogotá
dc.contributor.advisor | Jarrin Quintero, Jairo Alberto | |
dc.contributor.author | Rodríguez Vargas, Guillermo | |
dc.date.accessioned | 2023-07-31T20:23:53Z | |
dc.date.available | 2023-07-31T20:23:53Z | |
dc.date.issued | 2023-02-28 | |
dc.identifier.uri | http://hdl.handle.net/10818/56065 | |
dc.description | 50 páginas | es_CO |
dc.description.abstract | La congestión en los centros urbanos es un factor determinante en el bajo desempeño y en la pobre ejecución de los modelos logísticos de una ciudad. En Bogotá, la creciente congestión generada por una deficiente planeación urbanística, el incremento de las operaciones económicas y ambiciosos planes de construcción en infraestructura, han llevado a que Bogotá sea considerada como una de las ciudades más congestionadas en el mundo. Inclusive, el tiempo de desplazamiento en comparación con otras ciudades súper-congestionadas como lo son Sao Paulo y Ciudad de México es de 2.7 y 2.8 veces más en Bogotá que en las ciudades previamente mencionadas. Lo cual es determinante si consideramos que la población Bogotana es de 7 millones de habitantes, Sao Paulo tiene 12 millones de habitantes y Ciudad de México tiene más de 22 millones de habitantes. (Calatayud et al., 2021a) Bogotá al ser una ciudad súper-congestionada, presenta fuertes disminuciones en las velocidades de movilidad presentando como síntoma una eficiencia y eficacia totalmente disminuidas desde el punto de vista logístico, afectando los modelos de distribución en la ciudad e impactando directamente en la competitividad de las organizaciones que realizan actividades logísticas tanto dentro como alrededor de ella. Una limitada y disminuida velocidad de desplazamiento en la ciudad (considerada como una de las características más importantes en el desempeño logístico) en términos generales aumenta el lead time de entrega de un proveedor a un cliente. | 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 | Logística | |
dc.subject.other | Urbanismo | |
dc.title | Simulación del comportamiento del inventario frente a la reducción de lead time mediante estrategias de cargue y descargue en horarios no convencionales :Un caso de estudio en Bogotá | es_CO |
dc.type | master thesis | es_CO |
dc.type.hasVersion | publishedVersion | es_CO |
dc.rights.accessRights | openAccess | es_CO |
dc.subject.armarc | Transporte -- Planificación | |
dc.subject.armarc | Competencia económica | |
dc.subject.armarc | Proveedores y provisiones | |
dc.subject.armarc | Servicio al cliente | |
dcterms.references | Alan Davies, Shruti Lal, Fernando Perez, & Sanjhali Potdar. (2022). Defining ‘on-time, in-full’ in the consumer sector. McKinsey & Company Web Page. | |
dcterms.references | Alcaldia de Bogotá., & Secretaria de Movilidad. (2022). Bogotá está mejorando - Nuevas obras y movilidad sostenible | |
dcterms.references | Andriolo, A., Battini, D., Gamberi, M., Sgarbossa, F., & Persona, A. (2013). 1913-2013: The EOQ theory and next steps towards sustainability. IFAC Proceedings Volumes (IFAC PapersOnline), 46(9), 1708–1713. https://doi.org/10.3182/20130619-3-RU-3018.00371 | |
dcterms.references | APICS Members, & SCOR Practitioners. (2017). APICS Supply Chain Operations Reference Model SCOR Version 12.0. http://www.apics.org/docs/default-source/scor-training/scor v12-0-framework-introduction.pdf?sfvrsn=2 | |
dcterms.references | Calatayud, A., Sánchez González, S., Bedoya, F., Giraldez, M. F., & María Márquez, J. (2021a). Congestión urbana en América Latina y el Caribe: características, costos y mitigación. | |
dcterms.references | Calatayud, A., Sánchez González, S., Bedoya, F., Giraldez, M. F., & María Márquez, J. (2021b). Congestión urbana en América Latina y el Caribe: características, costos y mitigación. | |
dcterms.references | Castrellón, J. P. (2016). Off-Hour Deliveries in Bogotá Manager of the Public Private logistics office for Bogotá-Cundinamarca. | |
dcterms.references | Dias, P. A. P., Yoshizaki, H., Favero, P., & Vieira, J. G. V. (2019). Daytime or overnight deliveries? Perceptions of drivers and retailers in São Paulo City. Sustainability (Switzerland), 11(22). https://doi.org/10.3390/su11226316 | |
dcterms.references | Ekta, S., & Devendra, P. S. (s/f). Decongesting Urban Roads: An Investigation into Causes and Challenges. http://www.springer.com/series/15087 | |
dcterms.references | Estrada, M., Campos-Cacheda, J. M., & Robusté, F. (2018). Night deliveries and carrier-led consolidation strategies to improve urban goods distribution. Transport, 33(4), 930–947. https://doi.org/10.3846/transport.2018.6058 | |
dcterms.references | Fergusson, A. M., Orrego, J. E., Pava, D., Bocarejo, J. P., Nuñez, M., Duarte, J., & Ospina, M. (2019). Guía de buenas prácticas de cargue y descargue en horarios no convencionales en Bogotá. Ruta de implementación de iniciativas colaborativas. Alcaldia de Bogota. | |
dcterms.references | Fontoura, W. B., & Ribeiro, G. M. (2021). System dynamics for sustainable transportation policies: A systematic literature review. En Urbe (Vol. 13). Pontificia Universidade Catolica do Parana. https://doi.org/10.1590/2175-3369.013.E20200259 | |
dcterms.references | Galkin, A., Levada, V., Kyselov, V., Hulchak, O., Prunenko, D., & Voronko, I. (2022). Methods of Comparison of the Economic Order Quantity and Just-in-Time Restocking Technologies. The Case Study. Communications - Scientific Letters of the University of Zilina, 24(2), A35–A43. https://doi.org/10.26552/com.c.2022.2.a35-a43 | |
dcterms.references | Gambini, A., Mingari Scarpello, G., & Ritelli, D. (2013). Mathematical properties of EOQ models with special cost structure. Applied Mathematical Modelling, 37(3), 659–666. https://doi.org/10.1016/j.apm.2012.02.054 | |
dcterms.references | García-Laguna, J., San-José, L. A., Cárdenas-Barrón, L. E., & Sicilia, J. (2010). The integrality of the lot size in the basic EOQ and EPQ models: Applications to other production inventory models. Applied Mathematics and Computation, 216(5), 1660–1672. https://doi.org/10.1016/j.amc.2010.02.042 | |
dcterms.references | Glock, C. H. (2012). Lead time reduction strategies in a single-vendorsingle-buyer integrated inventory model with lot size-dependent lead times and stochastic demand. International Journal of Production Economics, 136(1), 37–44. https://doi.org/10.1016/j.ijpe.2011.09.007 | |
dcterms.references | Gómez Gaviria, D. (2020). Departamento Nacional de Planeación. Dirección de infraestructura y energía sosteni ble. Gran encuesta logística Colombiana | |
dcterms.references | Gonzalez, R. A., Ferro, R. E., & Liberona, D. (2020). Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia. Ain Shams Engineering Journal, 11(1), 25–34. https://doi.org/10.1016/j.asej.2019.05.002 | |
dcterms.references | Grace Hua, N., & Willems, S. P. (2016). Analytical insights into two-stage serial line supply chain safety stock. International Journal of Production Economics, 181, 107–112. https://doi.org/10.1016/j.ijpe.2015.10.010 | |
dcterms.references | Gutierrez, M., & Rivera, F. A. (2021). Undershoot and order quantity probability distributions in periodic review, reorder point, order-up-to-level inventory systems with continuous demand. Applied Mathematical Modelling, 91, 791–814. https://doi.org/10.1016/j.apm.2020.09.014 | |
dcterms.references | Hemalatha, S., & Annadurai, K. (2020a). Inventory models involving lead time crashing cost as an exponential function with ordering cost reduction dependent on lead time. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.10.271 | |
dcterms.references | Hemalatha, S., & Annadurai, K. (2020b). Inventory models involving lead time crashing cost as an exponential function with ordering cost reduction dependent on lead time. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.10.271 | |
dcterms.references | Heydari, J. (2014). Coordinating supplier’s reorder point: A coordination mechanism for supply chains with long supplier lead time. Computers and Operations Research, 48, 89–101. https://doi.org/10.1016/j.cor.2014.03.011 | |
dcterms.references | Iida, T. (2015). Benefits of leadtime information and of its combination with demand forecast information. International Journal of Production Economics, 163, 146–156. https://doi.org/10.1016/j.ijpe.2015.02.010 | |
dcterms.references | Jodlbauer, H., & Dehmer, M. (2020). An extension of the reorder point method by using advance demand spike information. Computers and Operations Research, 124. https://doi.org/10.1016/j.cor.2020.105055 | |
dcterms.references | Johansen, S. G. (2019). Emergency orders in the periodic-review inventory system with fixed ordering costs and stochastic lead times for normal orders. International Journal of Production Economics, 209, 205–214. https://doi.org/10.1016/j.ijpe.2018.01.017 | |
dcterms.references | Konstantaras, I., Skouri, K., & Lagodimos, A. G. (2019). EOQ with independent endogenous supply disruptions. Omega (United Kingdom), 83, 96–106. https://doi.org/10.1016/j.omega.2018.02.006 | |
dcterms.references | Li, X. (2020). Valuing lead-time and its variance in batch-ordering inventory policies. International Journal of Production Economics, 228. https://doi.org/10.1016/j.ijpe.2020.107731 | |
dcterms.references | Mangones M, S. C., García M, J. A., Holguín M, D. O., & Orejuela L, D. A. (2021). Differences in road-Traffic crash rates during construction and non-construction times on arterial streets: A comparative statistical analysis. Transportation Research Procedia, 58, 447–454. https://doi.org/10.1016/j.trpro.2021.11.060 | |
dcterms.references | Manuel Izar Landeta, J., Berenice Ynzunza Cortés, C., & Potosí, L. (2017). Estudio comparativo del cálculo del punto de reorden con la demanda y el tiempo de entrega poissonianos y correlacionados. (Vol. 38, Issue 5). | |
dcterms.references | Marin Martinez, F., Campuzano Bolarin, F., Cañas Sanchez, H., & Mula Bru, J. (2021). System Dynamics model for flow time and lot sizes optimization according to quick reponse manufacturing (QRM) strategy. DYNA, 96(1), 105–111. https://doi.org/10.6036/9661 | |
dcterms.references | Melis Teksan, Z., & Geunes, J. (2016). An EOQ model with price-dependent supply and demand. International Journal of Production Economics, 178, 22–33. https://doi.org/10.1016/j.ijpe.2016.04.023 | |
dcterms.references | Miranda, S., Fera, M., Iannone, R., & Riemma, S. (2015). A multi-item constrained EOQ calculation algorithm with exit condition: A comparative analysis. IFAC-PapersOnLine, 48(3), 1314–1319. https://doi.org/10.1016/j.ifacol.2015.06.267 | |
dcterms.references | Ozkan, O., & Kilic, S. (2019). A Monte Carlo simulation for reliability estimation of logistics and supply chain networks. IFAC-PapersOnLine, 52(13), 2080–2085. https://doi.org/10.1016/j.ifacol.2019.11.512 | |
dcterms.references | Pan, J. C. H., Lo, M. C., & Hsiao, Y. C. (2004). Optimal reorder point inventory models with variable lead time and backorder discount considerations. European Journal of Operational Research, 158(2), 488–505. https://doi.org/10.1016/S0377-2217(03)00366-7 | |
dcterms.references | Perera, S., Janakiraman, G., & Niu, S. C. (2017). Optimality of (s, S) policies in EOQ models with general cost structures. International Journal of Production Economics, 187, 216–228. https://doi.org/10.1016/j.ijpe.2016.09.017 | |
dcterms.references | Riezebos, J., & Zhu, S. X. (2020). Inventory control with seasonality of lead times. Omega (United Kingdom), 92. https://doi.org/10.1016/j.omega.2019.102162 | |
dcterms.references | Ronald H. Ballou. (2005). Logística Administración de la cadena de suministro. 5ta Edición. | |
dcterms.references | Sajadieh, M. S., & Eshghi, K. (2009). Sole versus dual sourcing under order dependent lead times and prices. Computers and Operations Research, 36(12), 3272–3280. https://doi.org/10.1016/j.cor.2009.03.001 | |
dcterms.references | Saoud, P., Kourentzes, N., & Boylan, J. E. (2022). Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation. Omega, 110, 102614. https://doi.org/10.1016/j.omega.2022.102614 | |
dcterms.references | Sevgen, A., & Sargut, F. Z. (2019). May reorder point help under disruptions? International Journal of Production Economics, 209, 61–69. https://doi.org/10.1016/j.ijpe.2018.02.014 | |
dcterms.references | van Wingerden, E., Basten, R. J. I., Dekker, R., & Rustenburg, W. D. (2014). More grip on inventory control through improved forecasting: A comparative study at three companies. International Journal of Production Economics, 157(1), 220–237. https://doi.org/10.1016/j.ijpe.2014.08.018 | |
dcterms.references | Xie, K., Ozbay, K., Yang, H., Holguín-Veras, J., & Morgul, E. F. (2015). Modeling safety impacts of off-hour delivery programs in urban areas. Transportation Research Record, 2478, 19–27. https://doi.org/10.3141/2478-03 | |
dcterms.references | Yang, G., Ronald, R. J., & Chu, P. (2005). Inventory models with variable lead time and present value. European Journal of Operational Research, 164(2), 358–366. https://doi.org/10.1016/j.ejor.2003.09.030 | |
dcterms.references | Zied, M., Baba¨ı, B., & Dallery, Y. (2005). An analysis of forecast based reorder point policies: The benefit of using forecasts. | |
thesis.degree.discipline | Escuela Internacional de Ciencias Económicas y Administrativas | es_CO |
thesis.degree.level | Maestría en Gerencia de Operaciones | es_CO |
thesis.degree.name | Magíster en Gerencia de Operaciones | es_CO |