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Neuroendocrine-Immune Regulation Based Approach for Disturbance Handling

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Adaptive Control of Bio-Inspired Manufacturing Systems

Part of the book series: Research on Intelligent Manufacturing ((REINMA))

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Abstract

A neuroendocrine-immune regulation-based approach for disturbance handling in BIMS is proposed. On the basis of mutual regulation mechanisms between nervous system, endocrine system and immune system, a disturbance handling approach for BIMS is proposed including the design of monitoring and scheduling functions for BIMC, disturbance detection and diagnosis mechanisms for BIMS, and strategies and concrete implementation steps. The results from comparative experiments verify that the disturbance handling approach improves the performance of the manufacturing system.

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References

  1. Cowling, P., & Johansson, M. (2002). Using real time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230–244.

    Article  MathSciNet  Google Scholar 

  2. Suresh, V., & Chaudhuri, D. (1993). Dynamic scheduling—A survey of research. International Journal of Production Economics, 32(1), 53–63.

    Article  Google Scholar 

  3. Stoop, P. P. M., & Wiers, V. C. S. (1996). The complexity of scheduling in practice. International Journal of Operations & Production Management, 16(10), 37–37.

    Google Scholar 

  4. Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Rescheduling manufacturing systems: A framework of strategies, policies, and methods. Journal of Scheduling, 6(1), 39–62.

    Article  MathSciNet  Google Scholar 

  5. Aytug, H., Lawley, M. A., McKay, K., et al. (2005). Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research, 161(1), 86–110.

    Article  MathSciNet  Google Scholar 

  6. Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. Project management and scheduling, 289–306. Elsevier.

    Google Scholar 

  7. Mehta, S. V., & Uzsoy, R. (1999). Predictable scheduling of a single machine subject to breakdowns. International Journal of Computer Integrated Manufacturing, 12(1), 15–38.

    Article  Google Scholar 

  8. Yang, Hongbin, & Yan, Hongsen. (2010). Deadlock-free scheduling of knowledgeable manufacturing cell with limited buffers. Systems Engineering Theory and Practice, 30(12), 2259–5568.

    Google Scholar 

  9. Lei, J., & Yang, Z. Y. (2013). Disturbance management design for a holonic multiagent manufacturing system by using hybrid approach. Applied Intelligence, 38(3), 267–278.

    Article  Google Scholar 

  10. Merdan, M., Moser, T., Sunindyo, W., et al. (2013). Workflow scheduling using multi-agent systems in a dynamically changing environment. Journal of Simulation, 7(3), 144–158.

    Article  Google Scholar 

  11. Lou, P., Liu, Q., Zhou, Z., et al. (2012). Multi-agent-based proactive-reactive scheduling for a job shop. The International Journal of Advanced Manufacturing Technology, 59(1–4), 311–324.

    Article  Google Scholar 

  12. Leitão, P. (2011). A holonic disturbance management architecture for flexible manufacturing systems. International Journal of Production Research, 49(5), 1269–1284.

    Article  Google Scholar 

  13. Jana, T., Naskar, S., Paul, S., et al. (2015). Handling machine breakdown for dynamic scheduling by a colony of cognitive agents in a holonic manufacturing framework. Decision Science Letters, 4(4), 509–524.

    Article  Google Scholar 

  14. Umar, U. A., Ariffin, M. K. A., Ismail, N., et al. (2015). Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. The International Journal of Advanced Manufacturing Technology, 81(9–12), 2123–2141.

    Article  Google Scholar 

  15. Erol, R., Sahin, C., Baykasoglu, A., et al. (2012). A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems. Applied Soft Computing, 12(6), 1720–1732.

    Article  Google Scholar 

  16. Zheng, K., Tang, D. B., Giret, A., et al. (2015). Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(S1), 121–134.

    Article  Google Scholar 

  17. Zheng, K., Tang, D. B., Giret, A., et al. (2018). A hormone regulation–based approach for distributed and on-line scheduling of machines and automated guided vehicles. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(1), 99–113.

    Article  Google Scholar 

  18. Cavalieri, S., Macchi, M., & Valckenaers, P. (2003). Benchmarking the performance of manufacturing control systems: Design principles for a web-based simulated test bed. Journal of Intelligent Manufacturing, 14(1), 43–58.

    Article  Google Scholar 

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Correspondence to Dunbing Tang .

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Tang, D., Zheng, K., Gu, W. (2020). Neuroendocrine-Immune Regulation Based Approach for Disturbance Handling. In: Adaptive Control of Bio-Inspired Manufacturing Systems. Research on Intelligent Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-15-3445-4_5

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