Bio-Inspired Manufacturing System Model

  • Dunbing TangEmail author
  • Kun Zheng
  • Wenbin Gu
Part of the Research on Intelligent Manufacturing book series (REINMA)


Nowadays manufacturing enterprises are forced to have manufacturing systems that can support the agile response to emergence and changing conditions. In a biological body, the neuroendocrine-immune system plays very important roles to control and modulate the adaptive behaviours using mutual regulation principles. Inspired by the regulation principles of the biological body, a novel concept of Bio-Inspired Manufacturing System (BIMS) is proposed which can agilely deal with the frequent occurrence of unexpected disturbances at the shop floor level. The control model of BIMS is described from the cybernetics point of view.


  1. 1.
    Ueda, K., Vaario, J., & Ohkura, K. (1997). Modeling of biological manufacturing systems for dynamic reconfiguration. Annals of the CIRP, 46, 343–346.CrossRefGoogle Scholar
  2. 2.
    Wiendahl, H. P., & Scholtissek, P. (1994). Management and control of complexity in manufacturing. Annals of the CIRP, 43, 533–540.CrossRefGoogle Scholar
  3. 3.
    Leitao, P. (2008). A bio-inspired solution for manufacturing control systems. In A. Azevedo (Ed.), Innovation in manufacturing (pp. 303–314) Boston: Springer.Google Scholar
  4. 4.
    Shen, W., & Norrie, D. H. (1999). Agent-based systems for intelligent manufacturing: A state-of-the-art survey. Knowledge and Information Systems, 1(2), 129–156.CrossRefGoogle Scholar
  5. 5.
    Brennan, R. W., Fletcher, M., & Norrie, D. H. (2002). An agent-based approach to reconfiguration of real-time distributed control systems. IEEE Transactions on Robotics and Automation, 18(4), 444–451.CrossRefGoogle Scholar
  6. 6.
    Wang, D. S., Nagalingam, S. V., & Lin, G. C. I. (2007). Development of an agent-based Virtual CIM architecture for small to medium manufacturers. Robotics and Computer Integrated Manufacturing, 23(1), 1–16.CrossRefGoogle Scholar
  7. 7.
    Ryu, K., & Jung, M. (2003). Agent-based fractal architecture and modeling for developing distributed manufacturing systems. International Journal of Production Research, 41(17), 4233–4255.CrossRefGoogle Scholar
  8. 8.
    Ryu, K., & Jung, M. (2003). Modeling and specifications of dynamic agents in fractal manufacturing systems. Computers in Industry, 52(2), 161–182.CrossRefGoogle Scholar
  9. 9.
    Brussel, H. Van, Wyns, J., Valckenaers, P., Bongaerts, L., & Peeters, P. (1998). Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry, 37(3), 255–274.CrossRefGoogle Scholar
  10. 10.
    Leitao, P., & Restivo, F. (2006). ADACOR: A holonic architecture for agile and adaptive manufacturing control. Computers in Industry, 57, 121–130.CrossRefGoogle Scholar
  11. 11.
    Colombo, A. W., Schoop, R., & Neubert, R. (2006). An agent-based intelligent control platform for industrial holonic manufacturing systems. IEEE Transactions on Industrial Electronics, 53(1), 322–337.CrossRefGoogle Scholar
  12. 12.
    Nahm, Y.-E., & Ishikawa, H. (2005). A hybrid multi-agent system architecture for enterprise integration using computer networks. Robotics and Computer-Integrated Manufacturing, 21, 217–234.CrossRefGoogle Scholar
  13. 13.
    Xiang, W., & Lee, H. P. (2008). Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Engineering Applications of Artificial Intelligence, 21, 73–85.CrossRefGoogle Scholar
  14. 14.
    Warnecke, H. J. (1993). The fractal company: A revolution in corporate culture. Berlin: Springer.CrossRefGoogle Scholar
  15. 15.
    Deen, S. M. (2003). Agent-based manufacturing: Advances in the holonic approach. Berlin: Springer.CrossRefGoogle Scholar
  16. 16.
    Okino, N. (1994). Bionic manufacturing system. Journal of Manufacturing Systems, 23(1), 175–187.Google Scholar
  17. 17.
    Wang, L., Tang, D. B., Gu, W. B., et al. (2012). Pheromone-based coordination for manufacturing system control. Journal of Intelligent Manufacturing, 23(3), 747–757.CrossRefGoogle Scholar
  18. 18.
    Farhy, L. S. (2004). Modeling of oscillations of endocrine networks with feedback. Methods Enzymology, 384, 54–81.CrossRefGoogle Scholar
  19. 19.
    Keenan, D. M., Licinio, J., & Veldhuis, J. D. (2001). A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitaryadrenal axis. PNAS, 98(7), 4028–4033.CrossRefGoogle Scholar
  20. 20.
    SureshKumar, N., & Sridharan, R. (2009). Simulation modeling and analysis of part and tool flow control decisions in a flexible manufacturing system. Robotics and Computer Integrated Manufacturing, 25, 829–838.CrossRefGoogle Scholar
  21. 21.
    Tharumarajah, A., Wells, A. J., & Nemes, L. (1996). Comparison of the bionic, fractal and holonic manufacturing system concepts. International Journal of Computer Integrated Manufacturing, 9(3), 217–226.CrossRefGoogle Scholar
  22. 22.
    Tang, D., Gu, W., et al. (2011). A neuroendocrine-inspired approach for adaptive manufacturing system control. International Journal of Production Research, 49(5), 1255–1268.CrossRefGoogle Scholar
  23. 23.
    Tu, X. Y., Wang, Z., & Guo, Y. W. (2005). Large systems cybernetics. Beijing: Press of Beijing University of Posts and Telecommunications.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Automotive and Rail Transit, Jiangsu Key Laboratory of Advanced Numerical Control TechnologyNanjing Institute of TechnologyNanjingChina
  3. 3.College of Mechanical and Electrical EngineeringHohai UniversityChangzhouChina

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