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Flexible Manufacturing Systems Optimization with Meta-heuristic Algorithm Using Open Source Software

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 763)

Abstract

This research develops the integration between the simulation of a flexible manufacturing system and the optimization of one of its resulting parameters by applying a meta-heuristic method based on genetic algorithms and discrete event simulation. The project focuses on the application of open-source software because the costs in implementing this integration with commercial software are high for the majority of existing companies in the country. The team used Jaamsim software in the simulation stage. It is a powerful simulator with a programmer-friendly graphical interface, developed in Java. For the construction and validation of the algorithm, the same program was used. The research provides a reference for the application of a tool that improves the productivity and competitiveness of companies in the market, focusing on the analysis of resources, planning, and observation in the behavior of a flexible manufacturing system to evaluate the accumulated times in their processes.

Keywords

  • Flexible manufacturing system
  • Genetic algorithm
  • Meta-Heuristic methods
  • Jaamsim
  • Open source software

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References

  1. Bernal, M., Sarmiento, C., Restrepo, J.: Productividad en una celda de manufactura flexible simulada en promodel utilizando path networks type crane. Universidad Distrital Francisco José de Caldas (2015). https://www.redalyc.org/pdf/2570/257036222011.pdf

  2. Caceres, R., Rivas, W.: Desarrollar un software planificador de rutas, para encontrar una ruta óptima, mediante algoritmos genéticos (jgap) con interfaz desarrollada en java. Unidad Académica de Ingeniería Civil - UTMACH (2017)

    Google Scholar 

  3. Caldas, A., Carpente, M., Lorenzo, S.: Aplicación de algoritmos heurísticos para optimizar el coste de doblaje de películas. Universidade da Coruña (2014)

    Google Scholar 

  4. Cruz, J., Badii, M.: SMED: El camino a la flexibilidad total, vol. 1 (2004)

    Google Scholar 

  5. EAE Business School: Tipos de sistemas de producción industrial y sus características (2018). https://retos-operaciones-logistica.eae.es/tipos-de-sistemas-de-produccion-industrial-y-sus-caracteristicas/

  6. Escobar, L.F.: Adaptive scheduling of flexible manufacturing systems with material handling constraints using GA and Colored Timed Petri Net. Japón (2015). http://www.f.waseda.jp/t-murata/Alumni.htm

  7. Flores, K., Medina, P.: Desarrollo de un software académico para programar la producción en los sistemas de manufactura flexible. Universidad Tecnológica de Pereira Facultad de Ingeniería Industrial (2013). http://recursosbiblioteca.utp.edu.co/tesisd/textoyanexos/0053F634.pdf

  8. García, E., Escobar, L.: Desarrollo de un simulador de sistemas de manufactura con interfaz gráfica basado en redes de petri. Revista iberoamericana de ingeniería mecánica (2018). https://dialnet.unirioja.es/servlet/articulo?codigo=6484717

  9. Gestal, M., Cebrián, D., Rabuñal, J., Pazos, A.: Introducción a los algoritmos genéticos y la programación genética, 1st edn. Universidade da Coruña, Servizo de Publicacións, España (2010)

    Google Scholar 

  10. Groover, M.: Introducción a los algoritmos genéticos y la programación genética, 1st edn. Prentice Hall Press Upper Saddle River, New Jersey (2002)

    Google Scholar 

  11. Huang, J., Segura, L.J., Wang, T., Zhao, G., Sun, H., Zhou, C.: Unsupervised learning for the droplet evolution prediction and process dynamics understanding in inkjet printing. Addit. Manuf. 35, 101197 (2020)

    Google Scholar 

  12. JaamSim Software Inc: JaamSim User Manual (2017). https://jaamsim.com/docs/JaamSim%20User%20Manual%202017-10.pdf

  13. Matt, D.T., Rauch, E., Dallasega, P.: Trends towards distributed manufacturing systems and modern forms for their design. Procedia cirp 33, 185–190 (2015)

    CrossRef  Google Scholar 

  14. Rabanal, P., Rodríguez, I., Rubio, F.: Algoritmos heurísticos y aplicaciones a métodos formales. Universidad Cumplutense de Madrid Facultad de Informática (2010). https://eprints.ucm.es/12027/1/T32515.pdf

  15. Segura, L.J., Zhao, G., Sun, H., Zhou, C.: Gaussian process tensor responses emulation for droplet solidification in freeze nano 3D printing of energy products. In: International Manufacturing Science and Engineering Conference, vol. 58745, p. V001T01A024. American Society of Mechanical Engineers (2019)

    Google Scholar 

  16. Segura, L.J., Zhao, G., Zhou, C., Sun, H.: Nearest neighbor gaussian process emulation for multi-dimensional array responses in freeze nano 3d printing of energy devices. J. Comput. Inf. Sci. Eng. 20(4), (2020)

    Google Scholar 

  17. Teruel, E., Aragüés, R.: Aprendiendo simulación de eventos discretos con jaamsim. pp. 522–527. Servicio de Publicaciones de la Universidad de Oviedo (2017). https://dialnet.unirioja.es/servlet/articulo?codigo=6591655

  18. Valtierra, J., Sausedo, J.: Reconfiguración autónoma de sistemas de manufactura mediante la optimización de funciones de desempeño del proceso. 12th Latin American and Caribbean Conference for Engineering and Technology (2014). http://www.laccei.org/LACCEI2014-Guayaquil/RefereedPapers/RP016.pdf

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Correspondence to Fabian Izquierdo .

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Izquierdo, F., Garcia, E., Cortez, B., Escobar, L. (2021). Flexible Manufacturing Systems Optimization with Meta-heuristic Algorithm Using Open Source Software. In: Botto Tobar, M., Cruz, H., Díaz Cadena, A. (eds) Recent Advances in Electrical Engineering, Electronics and Energy. CIT 2020. Lecture Notes in Electrical Engineering, vol 763. Springer, Cham. https://doi.org/10.1007/978-3-030-72212-8_18

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