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Production planning in 3D printing factories

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Production planning in 3D printing factories

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Nombre: De;Senovilla;González ...
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dc.contributor.author De Antón, J. es_ES
dc.contributor.author Senovilla, J. es_ES
dc.contributor.author González, J.M. es_ES
dc.contributor.author Acebes, F. es_ES
dc.contributor.author Pajares, J. es_ES
dc.date.accessioned 2020-07-23T09:29:48Z
dc.date.available 2020-07-23T09:29:48Z
dc.date.issued 2020-07-18
dc.identifier.uri http://hdl.handle.net/10251/148537
dc.description.abstract [EN] Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure. es_ES
dc.description.sponsorship This research has been partially financed by the project: “Lonja de Impresión 3D para la Industria 4.0 y la Empresa Digital (LONJA3D)” funded by the Regional Government of Castile and Leon and the European Regional Development Fund (ERDF, FEDER) with grant VA049P17. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof International Journal of Production Management and Engineering es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Additive manufacturing es_ES
dc.subject Production planning es_ES
dc.subject Packing problem es_ES
dc.subject Optimization es_ES
dc.subject Nesting es_ES
dc.title Production planning in 3D printing factories es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/ijpme.2020.12944
dc.relation.projectID info:eu-repo/grantAgreement/Junta de Castilla y León//VA049P17/ es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation De Antón, J.; Senovilla, J.; González, J.; Acebes, F.; Pajares, J. (2020). Production planning in 3D printing factories. International Journal of Production Management and Engineering. 8(2):75-86. https://doi.org/10.4995/ijpme.2020.12944 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/ijpme.2020.12944 es_ES
dc.description.upvformatpinicio 75 es_ES
dc.description.upvformatpfin 86 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 2340-4876
dc.relation.pasarela OJS\12944 es_ES
dc.contributor.funder Junta de Castilla y León es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.description.references Canellidis, V., Giannatsis, J., & Dedoussis, V. (2013). Efficient parts nesting schemes for improving stereolithography utilization. CAD Computer Aided Design, 45(5), 875-886. https://doi.org/10.1016/j.cad.2012.12.002 es_ES
dc.description.references Chergui, A., Hadj-Hamou, K., & Vignat, F. (2018). Production scheduling and nesting in additive manufacturing. Computers and Industrial Engineering, 126(May), 292-301. https://doi.org/10.1016/j.cie.2018.09.048 es_ES
dc.description.references Cui, Y. (2007). An exact algorithm for generating homogenous T-shape cutting patterns. Computers & Operations Research, 34(4), 1107-1120. https://doi.org/https://doi.org/10.1016/j.cor.2005.05.025 es_ES
dc.description.references Dvorak, F., Micali, M., & Mathieu, M. (2018). Planning and scheduling in additive manufacturing. Inteligencia Artificial, 21(62), 40-52. https://doi.org/10.4114/intartif.vol21iss62pp40-52 es_ES
dc.description.references Gogate, A. S., & Pande, S. S. (2008). Intelligent layout planning for rapid prototyping. International Journal of Production Research, 46(20), 5607-5631. https://doi.org/10.1080/00207540701277002 es_ES
dc.description.references Gupta, M. C., & Boyd, L. H. (2008). Theory of constraints: A theory for operations management. International Journal of Operations and Production Management, 28(10), 991-1012. https://doi.org/10.1108/01443570810903122 es_ES
dc.description.references Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European Journal of Operational Research, 88(1), 165-181. https://doi.org/10.1016/0377-2217(94)00166-9 es_ES
dc.description.references Kucukkoc, I. (2019). MILP models to minimise makespan in additive manufacturing machine scheduling problems. Computers and Operations Research, 105, 58-67. https://doi.org/10.1016/j.cor.2019.01.006 es_ES
dc.description.references Kucukkoc, I., Li, Q., & Zhang, D. Z. (2016). Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times. In 19th International Working Seminar on Production Economics (pp. 195-201). Innsbruck, Austria. es_ES
dc.description.references Li, Q., Kucukkoc, I., & Zhang, D. Z. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 1339-1351. https://doi.org/10.1016/j.cor.2017.01.013 es_ES
dc.description.references López-Paredes, A., Pajares, J., Martín, N., del Olmo, R., & Castillo, S. (2018). Application of combinatorial auctions to create a 3Dprinting market. Advancing in Engineering Network, Castro and Gimenez Eds. Lecture Notes in Management and Industrial Engineering (In Press), 12-13. es_ES
dc.description.references Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. https://doi.org/10.3390/app9183865 es_ES
dc.description.references Piili, H., Happonen, A., Väistö, T., Venkataramanan, V., Partanen, J., & Salminen, A. (2015). Cost Estimation of Laser Additive Manufacturing of Stainless Steel. Physics Procedia, 78(August), 388-396. https://doi.org/10.1016/j.phpro.2015.11.053 es_ES
dc.description.references Shaffer, S., Yang, K., Vargas, J., Di Prima, M. A., & Voit, W. (2014). On reducing anisotropy in 3D printed polymers via ionizing radiation. Polymer, 55(23), 5969-5979. https://doi.org/10.1016/j.polymer.2014.07.054 es_ES
dc.description.references Singhal, S. K., Pandey, A. P., Pandey, P. M., & Nagpal, A. K. (2005). Optimum Part Deposition Orientation in Stereolithography. Computer-Aided Design and Applications, 2(1-4), 319-328. https://doi.org/10.1080/16864360.2005.10738380 es_ES
dc.description.references Sung‐Hoon, A. (2002). Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal, 8(4), 248-257. https://doi.org/10.1108/13552540210441166 es_ES
dc.description.references Thomas, D. S., & Gilbert, S. W. (2015). Costs and cost effectiveness of additive manufacturing: A literature review and discussion. Additive Manufacturing: Costs, Cost Effectiveness and Industry Economics, 1-96. https://doi.org/10.6028/NIST.SP.1176 es_ES
dc.description.references Toro, E., Garces, A., & Ruiz, H. (2008). Two dimensional packing problem using a hybrid constructive algorithm of variable neighborhood search and simulated annealing. Revista Facultad de Ingeniería Universidad de Antioquia, 119-131. es_ES
dc.description.references Toro, E., & Granada-Echeverri, M. (2007). Problema de empaquetamiento rectangular bidimensional tipo guillotina resuelto por algoritmos genéticos. Scientia Et Technica. es_ES
dc.description.references Wang, Y., Zheng, P., Xu, X., Yang, H., & Zou, J. (2019). Production planning for cloud-based additive manufacturing-A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58(March), 145-157. https://doi.org/10.1016/j.rcim.2019.03.003 es_ES
dc.description.references Wodziak, J. R., Fadel, G. M., & Kirschman, C. (1994). A Genetic Algorithm for Optimizing Multiple Part Placement to Reduce Build Time. Proceedings of the Fifth International Conference on Rapid Prototyping., (May), 201,210. es_ES
dc.description.references Zhang, Y., Gupta, R. K., & Bernard, A. (2016). Two-dimensional placement optimization for multi-parts production in additive manufacturing. Robotics and Computer-Integrated Manufacturing, 38, 102-117. https://doi.org/10.1016/j.rcim.2015.11.003 es_ES
dc.description.references Zhao, Z., Zhang, L., & Cui, J. (2018). A 3D printing task packing algorithm based on rectangle packing in cloud manufacturing. Lecture Notes in Electrical Engineering, 460, 21-31. https://doi.org/10.1007/978-981-10-6499-9_3 es_ES
dc.description.references Zhou, L., Zhang, L., Laili, Y., Zhao, C., & Xiao, Y. (2018). Multi-task scheduling of distributed 3D printing services in cloud manufacturing. International Journal of Advanced Manufacturing Technology, 96(9-12), 3003-3017. https://doi.org/10.1007/s00170-017-1543-z es_ES
dc.description.references Zhou, L., Zhang, L., & Xu, Y. (2016). Research on the relationships of customized service attributes in cloud manufacturing. ASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016, 2, 1-8. https://doi.org/10.1115/MSEC2016-8530 es_ES


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