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A Generic Reconfigurable and Pluggable Material Handling System Based on Genetic Algorithm

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Service Orientation in Holonic and Multi-Agent Manufacturing (SOHOMA 2016)

Abstract

With the increasing customization of products there was a need to create new manufacturing systems that were able to satisfy these needs of the market. The Reconfigurable Manufacturing System (RMS) emerges as a more recent approach allowing the reconfiguration of the line and all the manufacturing systems. The balancing line is a common problem in the system reconfiguration but may not be enough, being also important to reconfigure the material handling system itself. Genetic Algorithms (GA) are one of the most known and used alternatives in optimization problems being widely used in shortest path problems like the travelling salesman. In this paper a solution that allows reconfiguring an agent based material handling system using a genetic algorithm is presented.

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References

  1. Rocha, A.: An agent based architecture for material handling systems (2013)

    Google Scholar 

  2. Setchi, R.M., Lagos, N.: Reconfigurability and reconfigurable manufacturing systems: state-of-the-art review, Ind. In: INDIN '04 2nd IEEE International Conference, Informatics, pp. 529–535 (2004)

    Google Scholar 

  3. El Maraghy, H.A.: Flexible and reconfigurable manufacturing systems paradigms. Flex. Serv. Manuf. J. 17(4), 261–276 (2006)

    Google Scholar 

  4. Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., Van Brussel, H.: Reconfigurable manufacturing systems. CIRP Ann. Manuf. Technol. 48(2), 527–540 (1999)

    Article  Google Scholar 

  5. Mukund Nilakantan, J., Ponnambalam, S.G.: An efficient PSO for type II robotic assembly line balancing problem. In: IEEE International Conference on Automation Science and Engineering (CASE12), vol. 56, no. 3, pp. 600–605 (2012)

    Google Scholar 

  6. Sabuncuoglu, I., Erel, E., Alp, A.: Ant colony optimization for the single model U-type assembly line balancing problem. Int. J. Prod. Econ. 120(2), 287–300 (2009)

    Article  Google Scholar 

  7. Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design, pp. 1–65 (1997)

    Google Scholar 

  8. Umar, U.A., Ariffin, M.K.A., Ismail, N., Tang, S.H.: Priority-based genetic algorithm for conflict-free automated guided vehicle routing. Procedia Eng. 50, 732–739 (2012)

    Article  Google Scholar 

  9. Yu, H., Shi, W.: A genetic algorithm for multi-model assembly line balancing problem. In: IEEE International Symposium on Assembly and Manufacturing (ISAM13), vol. 7, no. 1, pp. 369–371 (2013)

    Google Scholar 

  10. Xiaohong, L., Zhenyuan, J., Fuji, W., Wei, L.: Workshop material handling system lean planning based on genetic algorithm. Int. Symp. Comput. Intell. Design 1, 261–264 (2008)

    Google Scholar 

  11. Luo, M., Zhang, J.B., Wong, M.M., Zhuang, L.Q., Ng, K.C.: Computational intelligence approaches for improving serviceability of material handling systems, vol. 10, pp. 218–223 (2007)

    Google Scholar 

  12. De Oliveira, J.A.B., De Matos, P.L.C.: Coalition based approach for shop floor agility—a multiagent approach, Electrical Engineering and Robotics Integration Manufacturing, Universidade Nov. Lisboa, Fac. Ciências e Tecnol., vol. Ph. D., p. 329 (2003)

    Google Scholar 

  13. Beyer, T., Yousefifar, R., Abele, S., Bordasch, M., Wehking, K.H.: Flexible agent-based planning and adaptation of material handling systems. IEEE Int. Conf. Autom. Sci. Eng. 1060–1065 (2015)

    Google Scholar 

  14. Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-agent with JADE Systems (2007)

    Google Scholar 

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Correspondence to Andre Dionisio Rocha .

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Rocha, A.D., Caetano, P., Barata Oliveira, J. (2017). A Generic Reconfigurable and Pluggable Material Handling System Based on Genetic Algorithm. In: Borangiu, T., Trentesaux, D., Thomas, A., Leitão, P., Oliveira, J. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing . SOHOMA 2016. Studies in Computational Intelligence, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-51100-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-51100-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51099-6

  • Online ISBN: 978-3-319-51100-9

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