Aplicación de un modelo de planificación de la producción basado en técnicas de programación lineal y aprendizaje automático.
DOI:
https://doi.org/10.5935/jetia.v10i45.920Resumen
La ausencia de métodos de optimización eficientes combinados con conceptos de Inteligencia Artificial ha provocado ineficiencias y elevados costes en la planificación de la producción de las organizaciones. Así, este estudio tiene como objetivo optimizar la planificación de la producción en una empresa de equipos electrónicos, utilizando la Programación Lineal y el Aprendizaje Automático para apoyar decisiones asertivas y eficientes. El proceso metodológico comprende siete etapas: revisión de la literatura; Recopilación y análisis de datos de producción; Aplicación de métodos de Machine Learning para modelado; Selección del mejor modelo; Desarrollo y aplicación del modelo de Programación Lineal; Análisis de resultados; Validación con stakeholders. El enfoque resultó en una planificación de producción optimizada, capaz de reducir los costos operativos y ayudar en la toma de decisiones diaria de la organización. La técnica de previsión Machine Learning logró un error medio del 9%, lo que demuestra su precisión a la hora de pronosticar la demanda futura. Este estudio evidencia un enfoque sólido y prometedor para mejorar la eficiencia y eficacia en las operaciones de planificación de la producción. En este contexto, la unión entre Investigación Operativa y Aprendizaje Automático surge como una respuesta a las brechas existentes y una dirección impulsora para la optimización continua de estos procesos cruciales.
Descargas
Referencias
I.C. Baierle, J.L. Schaefer, M.A. Sellitto, J.C. Furtado, and E.O.B. Nara, “Moona software for survey classification and evaluation of criteria to support decision-making for properties portfolio”, International Journal of Strategic Property Management, vol.24, no.2, pp.226–236, 2020. DOI: https://doi.org/10.3846/ijspm.2020.12338
L.D.D. Nora, J.C.M. Siluk, A.L.N. Júnior, E.O.B. Nara, and J.C. Furtado, “The performance measurement of innovation and competitiveness in the telecommunications services sector”. International Journal of Business Excellence, 9(2), pp.210–224, 2016. DOI: https://doi.org/10.1504/IJBEX.2016.074844.
J.L. Schaefer, I.C. Baierle, M.A. Sellitto, J.C. Furtado, and E.O.B. Nara, “Competitiveness Scale as a Basis for Brazilian Small and Medium-Sized Enterprises”. EMJ - Engineering Management Journal, vol.33. no. 4, pp.255–271 2021. DOI: https://doi.org/10.1080/10429247.2020.1800385.
A. S. Pereira, M. C. Goncalves, E. O. B. Nara, andT. F. Ferraz, “Application of machine learning methods in forecasting sales prices in a project consultancy”. Produto e Produção. vol. 24, no. 2, pp. 1-12, 2023. DOI: https://doi.org/10.22456/1983-8026.127422.
M. C. Gonçalves, E. O. B. Nara, I. M. Dos Santos, I. B. Mateus, and L. M. B. do Amaral, “Comparative analysis of machine learning techniques via data mining in a railroad company In Deschamps, F.; Pinheiro de Lima, E.; Gouvêa da Costa, S.E.; G. Trentin, M. (Org.)”. Proceedings of the 11th International Conference on Production Research Americas. ICPR 2022. 1ed.Berlin: Springer Nature, vol. 1, pp. 655-664, 2022. Doi: 10.1007/978-3-031-36121-0_83.
M. Goncalves, A. Pereira, T. Ferraz, E. Nara, and I. Dias, “Predicting project sales prices using machine learning techniques: a case study in a project consultancy”. International Joint Conference on Industrial Engineering and Operations Management, IJCIEOM 2022. Springer Proceedings in Mathematics and Statistics 2023. Doi: http://dx.doi.org/10.14488.
B. Topu, and N. Cakici Alp, “Machine learning in architecture”, vol. 154, 2023.
S. Pandey, N. J. Basisth, T. Sachan, N. Kumari and P. Pakray, “Quantum machine learning for natural language processing application”, Physic A: Statistical Mechanics and its applications, vol. 627, 2023. doi: https://doi.org/10.1016/j.physa.2023.129123
H. Frederick and J. Lieberman. “Introduction to operations research”. Available at: My Library, (9th edition). Group A, 2013.
K. Hamasaki, M.C. Gonçalves, O. C. Junior, E. O. B. Nara, and R.R.G Wollmann, “Robust linear programming application for the production planning problem” In Deschamps, F.; Pinheiro de Lima, E.; Gouvêa da Costa, S.E.; G. Trentin, M. (Org.). Deschamps, F., Pinheiro de Lima, E., Gouvêa da Costa, S.E., G. Trentin, M. (eds) Proceedings of the 11th International Conference on Production Research Americas. ICPR 2022.. 1ed.Berlin: Springer Nature, 2022, vol. 1, pp. 647-654, 2022 Doi: 10.1007/978-3-031-36121-0_82.
I. Dias, R. Sampaio, R. Wollmann, M. Goncalves, and E. Nara, “A Decomposition Scheme in Production Planning Based on Linear Programming that Incorporates the Concept of a Dynamic Planning Environment”. International Joint Conference on Industrial Engineering and Operations Management, IJCIEOM 2022. Springer Proceedings in Mathematics and Statistics, 2023. http://dx.doi.org/10.14488/IJCIEOM2023_FULL_0031_37690.
M. Goncalves, R. Sampaio, R. Wollmann, E. Nara, and I. Dias, “Using robust approach concept to solve the production planning problem in manufacturing systems”. International Joint Conference on Industrial Engineering and Operations Management, IJCIEOM 2022. Springer Proceedings in Mathematics and Statistics 2023. http://dx.doi.org/10.14488/IJCIEOM2023_FULL_0031_37686.
M.C. Gonçalves, R. R. G. Wollmann, and R. J. B. Sampaio, “Proposal of a Numerical Approximation Theory to Solve the Robust Convex Problem of Production Planning”, Internacional Journal of Operational Research, 2023. Doi: 10.1504/IJOR.2022.10049618.
M. Gonçalves, A. Canciglieri, K. Strobel, M. Antunes, and R. Zanellato, “Application of operational research in process optimization in the cement industry”, JETIA, vol. 6, no. 24, pp. 36-40, Aug. 2020. Doi: https://doi.org/10.5935/jetia.v6i24.677.
G. de Faria, J. Tulik, and M. Gonçalves, “Proposition of A Lean Flow of Processes Based on The Concept of Process Mapping For A Bubalinocultura Based Dairy”, JETIA, vol. 5, no. 18, pp. 23-28, Jun. 2019. Doi 10.5935/2447-0228.20190022 (2019).
O. J. Junior and M. Gonçalves, “Application of Quality and Productivity Improvement Tools in A Potato Chips Production Line”, JETIA, vol. 5, no. 18, pp. 65-72, Jun. 2019.
F. Serra, E. Nara, M. Gonçalves, S. Costa, and S. Bortoluzzi, “Preliminary construct for decision making in organizations: a systemic approach”. In: Deschamps, F.; Pinheiro de Lima, E.; Gouvêa da Costa, S.E.; G. Trentin, M. (Org.). Proceedings of the 11th International Conference on Production Research Americas, 1ed.Berlin: Springer Nature, vol. 1, pp. 639-646, 2022. Doi: 10.1007/978-3-031-36121-0_81.
F. Lourenço, E. Nara, M. Goncalves, O. Canciglieri, “Preliminary Construct of Sustainable Product Development with a Focus on the Brazilian Reality: A Review and Bibliometric Analysis”. In: Leal Filho, W.; Frankenberger, F.;Tortato, U.. (Org.). Sustainability in Practice. 1ed.Suiça: Springer Nature, vol. 1, pp. 197-220, 2023. Doi: 10.1007/978-3-031-34436-7_12.
P.R.Tardio, J.L. Schaefer, E.O.B. Nara, M.C. Gonçalves, I.C.P. Dias, G.B. Benitez, and A. Castro e Silva, "The link between lean manufacturing and Industry 4.0 for product development process: a systemic approach", Journal of Manufacturing Technology Management, Vol. ahead-of-print No. ahead-of-print. 2023. https://doi.org/10.1108/JMTM-03-2023-0118.
G. Cardoso and I. C. Dias, "Mapping Process Improvement and Sequencing Analysis for Productive Definitions:" JETIA, vol. 6, no. 21, pp. 66-71, Feb. 2020.
F. Stankevecz and I. C. Dias, “System Integrated Management for Stock Management in a Beverage Distributor: A Proposal Based on A Case Study”, JETIA, vol. 5, no. 18, pp. 58-64, Jun. 2019.
