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dc.contributor.advisorJiménez Gordillo, José Fernando
dc.contributor.advisorVega Mejía, Carlos Alberto
dc.contributor.authorOrtiz Sáenz, Irma Rocío
dc.date.accessioned2023-10-04T14:40:21Z
dc.date.available2023-10-04T14:40:21Z
dc.date.issued2023-08-09
dc.identifier.urihttp://hdl.handle.net/10818/57582
dc.description119 páginases_CO
dc.description.abstractEste trabajo se encuentra enfocado en la mejora de la eficiencia operacional* 1 en una máquina de corte de plásticos en una empresa de fabricación de vidrio especializado. En este ambiente de producción, la variedad en la demanda de producción hace que los cambios de actividad sean recurrentes. En consecuencia, el problema objeto de este estudio es la secuenciación y programación de trabajos en una sola máquina minimizando makespan y tardanza ponderada en un ambiente de naturaleza estocástico. El proceso de corte de plásticos actualmente aporta el 60% en costo de los desperdicios de materia prima por falta de planificación de los trabajos. La metodología que se siguió en esta investigación fue la caracterización del proceso de corte de plásticos; revisión de literatura con las características del ambiente de una sola máquina; creación de un modelo de secuenciación y programación de trabajos para la máquina de corte de plásticos; y, por último, validación y evaluación del modelo creado por medio de un diseño de experimentos. Los resultados obtenidos fueron altamente significativos. En promedio, hubo una reducción del 2.7% en el desperdicio de plástico en comparación con el presupuesto proyectado. Además, la implementación condujo a una notable mejora en el tiempo de ejecución y la tardanza ponderada en comparación con los valores de ejecución anteriores.es_CO
dc.description.abstractThis work is focused on improving the operational efficiency of a plastic cutting machine in a specialized glass manufacturing company. In this production environment, the variety in production demand makes the activity changes recurrent. Consequently, the problem under study is the sequencing and scheduling of jobs on a single machine minimizing makespan and weighted tardiness in a stochastic environment. The plastic-cutting process currently contributes 60% in cost of raw material waste due to a lack of job planning. The methodology followed in this research was the characterization of the plastic-cutting process; literature review with the characteristics of the environment of a single machine; creation of a job sequencing and scheduling model for the plastic cutting machine; and, lastly, validation and evaluation of the model were created through a design of experiments. The results obtained were highly significant. On average, there was a reduction of 2.7% in plastic waste compared to the projected budget. Additionally, the implementation led to a notable improvement in makespan and weighted tardiness when compared to previous execution values.en
dc.formatapplication/pdfes_CO
dc.language.isospaes_CO
dc.publisherUniversidad de La Sabanaes_CO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceUniversidad de La Sabanaes_CO
dc.sourceIntellectum Repositorio Universidad de La Sabanaes_CO
dc.subject.otherOptimización multiobjetivo
dc.subject.otherAnálisis estocástico
dc.subject.otherSecuenciación
dc.subject.otherTardanza ponderada
dc.titleProgramación de la producción para la minimización del Makespan y la tardanza total ponderada en una empresa de fabricación de vidrio especializadoes_CO
dc.typemaster thesises_CO
dc.type.hasVersionpublishedVersiones_CO
dc.rights.accessRightsrestrictedAccesses_CO
dcterms.referencesAbc S.A. (ABC). (2020a). Indicadores 2 3Q 2020 costo EQ NIII. Autor.
dcterms.referencesAbc S.A. (ABC). (2020b). Optimización de plásticos V2. Autor.
dcterms.referencesAbc S.A. (ABC). (2020c). Planeación Estratégica 2020. Autor.
dcterms.referencesAbc S.A. (ABC). (2020d). Tiempo de servicio pedidos 2020. Autor.
dcterms.referencesAbc S.A. (ABC). (2022). Financieros 2022. Autor.
dcterms.referencesAbc S.A. (ABC). (2023a). Histórico Yield Co 2023. Autor.
dcterms.referencesAbc S.A. (ABC). (2023b). Reporte de optimización PU, PowerBI 2023. Autor.
dcterms.referencesAbc S.A. (ABC). (2023c). Histórico Cumplimiento Co 2023. Autor.
dcterms.referencesAbc S.A. (ABC). (2023d). Consolidado informes de gestión 2020-2023. Autor.
dcterms.referencesAzizoglu, M., & Kondakci, S., & Köksalan, M. (2003). Single machine scheduling with maximum earliness and number tardy. Comput Ind Eng 45:257–268 doi:10.1016/S0360- 8352(03)00034-2
dcterms.referencesBrauner, N., Finke, G. & Shafransky, Y. Lawler’s minmax cost problem under uncertainty. J Comb Optim 34, 31–46 (2017). https://doi-org.ez.unisabana.edu.co/10.1007/s10878-016- 0051-7
dcterms.referencesBriskorn, D., Stephan, K., & Boysen, N. (2022). Minimizing the makespan on a single machine subject to modular setups. Journal of Scheduling, 25(1), 125-137. doi:10.1007/s10951-021- 00704-8
dcterms.referencesBritish Standards Institution (2000). BS EN 1063: 2000. Glass in building – Security glazing – testing and classification of resistance against bullet attack. CEN, Brussels.
dcterms.referencesChen, W. J. (2007). An efficient algorithm for scheduling jobs on a machine with periodic maintenance. Int J Adv Manuf Technol, 34:1173–1182 doi:10.1007/s00170-006-0689-x
dcterms.referencesEl Cadi, A. A., Benmansour, R., Serraj, F., & Artiba, A. (2016). A joint optimization-simulation model to minimize the makespan on a repairable machine. Paper presented at the Proceedings of 2015 International Conference on Industrial Engineering and Systems Management, IEEE IESM 2015, 489-495. doi:10.1109/IESM.2015.7380203
dcterms.referencesEren, T., & Güner, E. (2006). A bicriteria scheduling with sequencedependent setup times. Appl
dcterms.referencesMath Comput, 179:378–385, doi:10.1016/j.amc.2005.11.112
dcterms.referencesFigueira, G., & Almada-Lobo, B. (2014). Hybrid simulation-optimization methods: A taxonomy and discussion. Simulation Modelling Practice and Theory, 46, 118–134. https://doi.org/10.1016/j.simpat.2014.03.007
dcterms.referencesFigueira, G., Furlan, M., & Almada-Lobo, B. (2013). Predictive production planning in an integrated pulp and paper mill. IFAC Proceedings Volumes, 46(9), 371–376. https://doi.org/10.3182/20130619-3-RU-3018.00409
dcterms.referencesFirat, M., Meyere, D. J., Martagan, T., & Genga, L. (2022). Optimizing the workload of production units of a make-to-order manufacturing system, Computers & Operations Research, vol. 138, https://doi.org/10.1016/j.cor.2021.105530.
dcterms.referencesFridman, I., Pesch, E., & Shafransky, Y. (2020). Minimizing maximum cost for a single machine under uncertainty of processing times. European Journal of Operational Research, vol. 286, Issue 2, pp 444-457, ISSN 0377-2217, https://doi.org/10.1016/j.ejor.2020.03.052.
dcterms.referencesGreeff, G., & Ghoshal, R. (Eds.). (2004). Practical E-Manufacturing and Supply Chain Management. https://doi.org/10.1016/B978-075066272-7/50009-9.
dcterms.referencesGupta, A. K., & Sivakumar A. I. (2005). Single machine scheduling with multiple objectives in semiconductor manufacturing. Int J Adv Manuf Technol 26:950–958 doi:10.1007/s00170- 004-2074-y
dcterms.referencesHaral, U., Chen, R. W., Ferrell, W. G. J., & Kurz, M. B. (2007). Multiobjective single machine scheduling with non-traditional requirements. Int J Prod Econ 106:574–484 doi:10.1016/j.ijpe.2006.06.018
dcterms.referencesHo, I., & Do, B. (2020). Bi-objective optimization approach for energy aware scheduling considering electricity cost and preventive maintenance using genetic algorithm. Journal of Cleaner Production, vol. 244, 118869, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2019.118869.
dcterms.referencesJolai, F., Rabbani, M., Amalnick, S., Dabaghi, A., Dehghan, M., & Parast, M. Y. (2007). Genetic algorithm for bi-criteria single machine scheduling problem of minimizing maximum earliness and number of tardy jobs. Appl Math Comput, 194:552–560 doi:10.1016/j.amc.2007.04.063
dcterms.referencesJuan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics:
dcterms.referencesExtending metaheuristics to deal with stochastic combinatorial optimization problems, Operations Research Perspectives, Volume 2, Pages 62-72, ISSN 2214-7160, https://doi.org/10.1016/j.orp.2015.03.001
dcterms.referencesKöksalan, M., & Keha, A. B. (2003). Using genetic algorithms for single-machine bicriteria scheduling problems, European Journal of Operational Research, vol. 145, issue 3, 543-556, ISSN 0377-2217, https://doi.org/10.1016/S0377-2217(02)00220-5.
dcterms.referencesLe, C., Ladier, A., Botta-Genoulaz, V., & Laforest,V. (2018). Reducing waste in manufacturing operations: a mixed integer linear program for bi-objective scheduling on a single-machine with coupled-tasks. IFAC-PapersOnLine, vol. 51, Issue 11, pp 1695-1700, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2018.08.212.
dcterms.referencesLei, D. (2009). Multi-objective production scheduling: a survey. Int J Adv Manuf Technol, vol. 43, 926-938. https://doi-org.ez.unisabana.edu.co/10.1007/s00170-008-1770-4
dcterms.referencesLi, H., Gajpal, Y., & Bector, C. R. (2020). A survey of due-date related single-machine with twoagent scheduling problem. Journal of Industrial & Management Optimization, vol. 16, no. 3, 1329-1347. doi:10.3934/jimo.2019005
dcterms.referencesLin, S. W., & Ying, K. C. (2022). Single machine scheduling problems with sequence-dependent setup times and precedence delays. Scientific Reports, 12(1) doi:10.1038/s41598-022-13278- y
dcterms.referencesLu, C. C., Lin, S. W., & Ying, K. C. (2014). Robust single machine scheduling for minimizing total flow time in the presence of uncertain processing times. Computers & Industrial Engineering, vol 74, pp 102-110, ISSN 0360-8352, https://doi.org/10.1016/j.cie.2014.04.013.
dcterms.referencesMoghaddam, K. S. (2013). Multi-objective preventive maintenance and replacement scheduling in a manufacturing system using goal programming, International Journal of Production Economics, Volume 146, Issue 2, Pages 704-716, ISSN 0925-5273, https://doi.org/10.1016/j.ijpe.2013.08.027
dcterms.referencesMoghaddam, K. S. (2013). Multi-objective preventive maintenance and replacement scheduling in a manufacturing system using goal programming, International Journal of Production Economics, Volume 146, Issue 2, Pages 704-716, ISSN 0925-5273, https://doi.org/10.1016/j.ijpe.2013.08.027
dcterms.referencesNong, Q., Yuan, J., Fu, R., Lin, L., & Tian, J. (2008). The single-machine parallel-batching online scheduling problem with family jobs to minimize makespan. International Journal of Production Economics, 111, 435-440.
dcterms.referencesOsnes, K., Holmen, J. K., Grue, T., & Børvik, T. (2021). Perforation of laminated glass: An experimental and numerical study. International Journal of Impact Engineering, vol. 156. https://doi.org/10.1016/j.ijimpeng.2021.103922.
dcterms.referencesPedeliento, G., Andreini, D., Bergamaschi, M., & Salo, J. (2015). Brand and product attachment in an industrial context: The effects on brand loyalty. Journal of Industrial Marketing Management, vol. 53, 194-206. http://dx.doi.org/10.1016/j.indmarman.2015.06.007
dcterms.referencesPinedo, M. (2008). Scheduling, Theory, algorithms, and systems (3er ed). Springer, New York.
dcterms.referencesRaileanu, S., Borangiu, T., Morariu, O., & Stocklosa, O. (2014). ILOG-based mixed planning and scheduling system for job-shop manufacturing. In 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, pp. 1-6
dcterms.referencesRao, Y., Huang, G., Li, P., Shao, X., & Yu, D. (2007). An integrated manufacturing information system for mass sheet metal cutting. International Journal of Advanced Manufacturing Technology, 33(5-6), 436-448. doi:10.1007/s00170-006-0484-8
dcterms.referencesSenthilkumar, P., & Narayanan, S. (2010). Literature Review of Single Machine Scheduling Problem with Uniform Parallel Machines. Intelligent Information Management, Vol. 2 No. 8, pp. 457-474. doi: 10.4236/iim.2010.28056.
dcterms.referencesShen, J., & Zhu, K. (2018). An uncertain single machine scheduling problem with periodic maintenance. Knowledge-Based Systems, vol. 144, pp 32-41, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2017.12.021.
dcterms.referencesSlack, N., Chambers, S., & Johnston, R. (2010). Operations management. Pearson Education.
dcterms.referencesTavakkoli-Moghaddam, R., Javadi, B., Jolai, F., & Ghodratnama, A. (2010). The use of a fuzzy multi-objective linear programming for solving a multi-objective single-machine scheduling problem, Applied Soft Computing, Volume 10, Issue 3, Pages 919-925, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2009.10.010.
dcterms.referencesThapar, A., Pandey, D., & Gaur, S.K., (2012). Satisficing solutions of multi-objective fuzzy optimization problems using genetic algorithm, Applied Soft Computing, Volume 12, Issue 8, 2012, Pages 2178-2187, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2012.03.002.
dcterms.referencesWang, J. B., Jia, X., Yan, J. X., Wang, S. H., & Qian, J. (2022). Single machine group scheduling problem with makespan objective and a proportional linear shortening. RAIRO - Operations
dcterms.referencesResearch, 56(3), 1523-1532. doi:10.1051/ro/2022078
dcterms.referencesWang, S., Liu, M., Chu, F., & Chu, C. (2016) Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration. Journal of Cleaner Production, vol. 137, pp 1205-1215, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2016.07.206.
dcterms.referencesYin, Y., Wu, C., Wu, W., & Cheng, S. (2011). The single-machine total weighted tardiness scheduling problem with position-based learning effects. Computers & Operations Research, 39, 1109-1116.
dcterms.referencesYuan, X., Jian-ying, X., & Xiao-long, D. (2005). A single machine scheduling problem with fuzzy time delays. IFAC Proceedings Volumes, vol. 38, Issue 1, pp 37-42, ISSN 1474-6670, ISBN 9783902661753, https://doi.org/10.3182/20050703-6-CZ-1902.01490
dcterms.referencesZheng, J., Chen, A., Zheng, W., Zhou, X., Bai, B., Wu, J., Ling, W., Ma, H., &Wang, W. (2020). Effectiveness analysis of resources consumption, environmental impact and production efficiency in traditional manufacturing using new technologies: Case from sand casting. Journal of Energy Conversion and Management, vol. 209, https://doi.org/10.1016/j.enconman.2020.112671
thesis.degree.disciplineEscuela Internacional de Ciencias Económicas y Administrativases_CO
thesis.degree.levelMaestría en Gerencia de Operacioneses_CO
thesis.degree.nameMagíster en Gerencia de Operacioneses_CO


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Attribution-NonCommercial-NoDerivatives 4.0 InternacionalExcept where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional