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Modelo para la programación de facturación por regionales para el proceso de alistamiento de pedidos en una compañía de venta directa
dc.contributor.advisor | Quintero Araujo, Carlos Leonardo | |
dc.contributor.author | González Torres, Fabio Andrés | |
dc.date.accessioned | 2019-09-25T15:48:30Z | |
dc.date.available | 2019-09-25T15:48:30Z | |
dc.date.issued | 2019-08-07 | |
dc.identifier.uri | http://hdl.handle.net/10818/37435 | |
dc.description | 64 páginas | es_CO |
dc.description.abstract | Dentro de la cadena de abastecimiento de una empresa se encuentra el macroproceso de distribución en el cual existen costos asociados al alistamiento de los pedidos de los clientes. Estos costos pueden ser muy elevados si no se tiene un proceso controlado con objetivos claros de eficiencia. Esta investigación trata el problema de programación de facturación por regionales para el alistamiento de pedidos en una compañía de venta directa en el centro de distribución que atiende a los clientes de Colombia, los pedidos son realizados para cada una de las 18 campañas del año. La investigación plantea un modelo de optimización que permita encontrar, a través de un método exacto o heurístico, una mejor solución (cronograma de programación de pedidos por regional) con respecto a la actual, en cuanto a costo de mano de obra operativa del centro de distribución, garantizando el cumplimiento en los tiempos prometidos de entrega a los clientes y sin evaluar un eventual faltante asociado a disponibilidad de producto. Con base en lo anterior, se contempla como supuesto la disponibilidad constante de todas las referencias para incluir en el pedido de los clientes. | 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 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | Universidad de La Sabana | |
dc.source | Intellectum Repositorio Universidad de La Sabana | |
dc.subject | Cadena de suministros | es_CO |
dc.subject | Distribución física de mercancías | es_CO |
dc.subject | Venta directa | es_CO |
dc.subject | Administración de personal | es_CO |
dc.title | Modelo para la programación de facturación por regionales para el proceso de alistamiento de pedidos en una compañía de venta directa | es_CO |
dc.type | masterThesis | es_CO |
dc.publisher.program | Maestría en Gerencia de Operaciones | es_CO |
dc.publisher.department | Escuela Internacional de Ciencias Económicas y Administrativas | es_CO |
dc.identifier.local | 274011 | |
dc.identifier.local | TE10319 | |
dc.type.hasVersion | publishedVersion | es_CO |
dc.rights.accessRights | restrictedAccess | es_CO |
dc.creator.degree | Magíster en Gerencia de Operaciones | es_CO |
dcterms.references | Ardjmand, E., Shakeri, H., Singh, M., & Sanei, O. (2018). Minimizing order picking makespan with multiple pickers in a wave picking warehouse. Intern. Journal of Production Economics, 206(March), 169–183. https://doi.org/10.1016/j.ijpe.2018.10.001 | eng |
dcterms.references | Boysen, N., Briskorn, D., & Emde, S. (2017). Sequencing of picking orders in mobile rack warehouses. European Journal of Operational Research, 259(1), 293–307. https://doi.org/10.1016/j.ejor.2016.09.046 | eng |
dcterms.references | Chan, F. T. S., Wang, Z., Singh, Y., Wang, X. P., Ruan, J. H., & Tiwari, M. K. (2019). Activity scheduling and resource allocation with uncertainties and learning in activities. Industrial Management & Data Systems, 0(0), null. https://doi.org/10.1108/IMDS-01-2019-0002 | eng |
dcterms.references | Chen, T. L., Cheng, C. Y., Chen, Y. Y., & Chan, L. K. (2015). An efficient hybrid algorithm for integrated order batching, sequencing and routing problem. International Journal of Production Economics, 159, 158–167. https://doi.org/10.1016/j.ijpe.2014.09.029 | eng |
dcterms.references | de Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481–501. https://doi.org/10.1016/j.ejor.2006.07.009 | eng |
dcterms.references | Doganis, P., & Sarimveis, H. (2007). Optimal scheduling in a yogurt production line based on mixed integer linear programming. Journal of Food Engineering, 80(2), 445–453. https://doi.org/https://doi.org/10.1016/j.jfoodeng.2006.04.062 | eng |
dcterms.references | Dong, C., Yang, Y., & Zhao, M. (2018). Dynamic selling strategy for a firm under asymmetric information: Direct selling vs. agent selling. International Journal of Production Economics, 204, 204–213. https://doi.org/https://doi.org/10.1016/j.ijpe.2018.07.034 | eng |
dcterms.references | Dumond, E. J. (2005). Understanding and using the capabilities of finite scheduling. Industrial Management & Data Systems, 105(4), 506–526. https://doi.org/10.1108/02635570510592398 | eng |
dcterms.references | Elbert, R. M., Franzke, T., Glock, C. H., & Grosse, E. H. (2017). The effects of human behavior on the efficiency of routing policies in order picking: The case of route deviations. Computers and Industrial Engineering, 111, 537–551. https://doi.org/10.1016/j.cie.2016.11.033 | eng |
dcterms.references | Euromonitor International. (2018a). Sector Capsule : Colour Cosmetics in Colombia. | eng |
dcterms.references | Euromonitor International. (2018b). Sector Capsule : Deodorants in Colombia. | spa |
dcterms.references | Euromonitor International. (2018c). Sector Capsule : Fragances in Colombia. | fr |
dcterms.references | Euromonitor International. (2018d). Sector Capsule : Mass Beauty and Personal Care in Colombia. | eng |
dcterms.references | Euromonitor International. (2018e). Sector Capsule : Skin Care in Colombia. | eng |
dcterms.references | Ferrell, L., & Ferrell, O. C. (2012). Redirecting direct selling: High-touch embraces high-tech. Business Horizons, 55(3), 273–281. https://doi.org/https://doi.org/10.1016/j.bushor.2012.01.004 | eng |
dcterms.references | Füßler, D., & Boysen, N. (2017). Efficient order processing in an inverse order picking system. Computers and Operations Research, 88, 150–160. https://doi.org/10.1016/j.cor.2017.07.005 | eng |
dcterms.references | GAMS Development Corporation. (n.d.). An Introduction to GAMS. Retrieved April 22, 2019, from https://www.gams.com/products/introduction/ | eng |
dcterms.references | Giannikas, V., Lu, W., Robertson, B., & McFarlane, D. (2017). An interventionist strategy for warehouse order picking: Evidence from two case studies. International Journal of Production Economics, 189(April 2016), 63–76. https://doi.org/10.1016/j.ijpe.2017.04.002 | eng |
dcterms.references | Henn, S. (2012). Algorithms for on-line order batching in an order picking warehouse. Computers and Operations Research, 39(11), 2549–2563. https://doi.org/10.1016/j.cor.2011.12.019 | eng |
dcterms.references | Ho, Y.-C., & Lin, J.-W. (2017). Improving order-picking performance by converting a sequential zone-picking line into a zone-picking network. Computers & Industrial Engineering, 113, 241– 255. https://doi.org/https://doi.org/10.1016/j.cie.2017.09.014 | eng |
dcterms.references | Lu, W., McFarlane, D., Giannikas, V., & Zhang, Q. (2016). An algorithm for dynamic order-picking in warehouse operations. European Journal of Operational Research, 248(1), 107–122. https://doi.org/10.1016/j.ejor.2015.06.074 | eng |
dcterms.references | Pansart, L., Catusse, N., & Cambazard, H. (2018). Exact algorithms for the order picking problem. Computers and Operations Research, 100, 117–127. https://doi.org/10.1016/j.cor.2018.07.002 | eng |
dcterms.references | Quader, S., & Castillo-Villar, K. K. (2018). Design of an enhanced multi-aisle order-picking system considering storage assignments and routing heuristics. Robotics and Computer-Integrated Manufacturing, 50(April 2015), 13–29. https://doi.org/10.1016/j.rcim.2015.12.009 | eng |
dcterms.references | Scholz, A., Schubert, D., & Wäscher, G. (2017). Order picking with multiple pickers and due dates – Simultaneous solution of Order Batching, Batch Assignment and Sequencing, and Picker Routing Problems. European Journal of Operational Research, 263(2), 461–478. https://doi.org/10.1016/j.ejor.2017.04.038 Sectorial. (2018). Informe Sector Cosmético en Colombia. | eng |
dcterms.references | Sel, Ç., Bilgen, B., & Bloemhof-Ruwaard, J. (2017). Planning and scheduling of the make-and-pack dairy production under lifetime uncertainty. Applied Mathematical Modelling, 51, 129–144. https://doi.org/https://doi.org/10.1016/j.apm.2017.06.002 | eng |
dcterms.references | Tarapuez, J., & Barrera, G. (2010). GAMS Aplicado a las Ciencias económicas GAMS Aplicado a las Ciencias económicas. Universidad Nacional de Colombia. Retrieved from http://www.fce.unal.edu.co/uifce/proyectos-de-estudio/pdf/GAMS aplicado a las Ciencias Economicas | eng |
dcterms.references | Thomas, L. M., & Meller, R. D. (2015). Developing design guidelines for a case-picking warehouse. International Journal of Production Economics, 170, 741–762. https://doi.org/https://doi.org/10.1016/j.ijpe.2015.02.011 | eng |
dcterms.references | van Gils, T., Ramaekers, K., Braekers, K., Depaire, B., & Caris, A. (2018). Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. International Journal of Production Economics, 197(June 2016), 243–261. https://doi.org/10.1016/j.ijpe.2017.11.021 | eng |
dcterms.references | van Gils, T., Ramaekers, K., Caris, A., & de Koster, R. B. M. (2018). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European Journal of Operational Research, 267(1), 1–15. https://doi.org/10.1016/j.ejor.2017.09.002 | eng |
dcterms.references | Velez, S., Dong, Y., & Maravelias, C. T. (2017). Changeover formulations for discrete-time mixedinteger programming scheduling models. European Journal of Operational Research, 260(3), 949–963. https://doi.org/https://doi.org/10.1016/j.ejor.2017.01.004 | eng |
dcterms.references | Velez, S., Merchan, A. F., & Maravelias, C. T. (2015). On the solution of large-scale mixed integer programming scheduling models. Chemical Engineering Science, 136, 139–157. https://doi.org/https://doi.org/10.1016/j.ces.2015.05.021 | eng |
dcterms.references | Wisconsin Institute for Discovery. (n.d.). NEOS Server: State-of-the-Art Solvers for Numerical Optimization. Retrieved April 22, 2019, from https://neos-server.org/neos/ | eng |
dcterms.references | Zhang, J., Wang, X., & Huang, K. (2018). On-line scheduling of order picking and delivery with multiple zones and limited vehicle capacity. Omega (United Kingdom), 79, 104–115. https://doi.org/10.1016/j.omega.2017.08.004 | eng |