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Reconfiguration point decision method based on dynamic complexity for reconfigurable manufacturing system (RMS)

Author

Listed:
  • Sihan Huang

    (Beijing Institute of Technology)

  • Guoxin Wang

    (Beijing Institute of Technology)

  • Xiwen Shang

    (Beijing Institute of Technology)

  • Yan Yan

    (Beijing Institute of Technology)

Abstract

To address the problem of how to identify the best time to implement reconfiguration for the reconfigurable manufacturing system (RMS), a dynamic complexity-based RMS reconfiguration point decision method is proposed. This method first identifies factors that affect RMS dynamic complexity (including both positive and negative complexity) at the machine tool and manufacturing cell levels. Next, based on information entropy theory, a quantitative model for RMS dynamic complexity is created, which is solved via state probability analysis for processing capability and the processing function. This model is combined with cusp catastrophe theory to establish an RMS reconfiguration decision model. Both positive and negative complexity are control variables for cusp catastrophe. Cusp catastrophe’s state condition is used to identify RMS state catastrophe at the final stage of production. This catastrophe point is the RMS reconfiguration point. Finally, the case study result shows that this method can effectively identify the RMS state catastrophe moment so that system reconfiguration is implemented promptly to improve RMS’s responsiveness to the market.

Suggested Citation

  • Sihan Huang & Guoxin Wang & Xiwen Shang & Yan Yan, 2018. "Reconfiguration point decision method based on dynamic complexity for reconfigurable manufacturing system (RMS)," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1031-1043, June.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:5:d:10.1007_s10845-017-1318-2
    DOI: 10.1007/s10845-017-1318-2
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    References listed on IDEAS

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    1. Mostafa G. Mehrabi, A.Galip Ulsoy, Yoram Koren, 2000. "Reconfigurable manufacturing systems and their enabling technologies," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 1(1), pages 114-131.
    2. Dou, Wenyu & Ghose, Sanjoy, 2006. "A dynamic nonlinear model of online retail competition using Cusp Catastrophe Theory," Journal of Business Research, Elsevier, vol. 59(7), pages 838-848, July.
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    Cited by:

    1. D.-Y. Kim & J.-W. Park & S. Baek & K.-B. Park & H.-R. Kim & J.-I. Park & H.-S. Kim & B.-B. Kim & H.-Y. Oh & K. Namgung & W. Baek, 2020. "A modular factory testbed for the rapid reconfiguration of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 661-680, March.
    2. Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.
    3. Zhenjun Ming & Cong Zeng & Guoxin Wang & Jia Hao & Yan Yan, 2020. "Ontology-based module selection in the design of reconfigurable machine tools," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 301-317, February.
    4. Germán Herrera Vidal & Jairo R. Coronado-Hernández & Claudia Minnaard, 2023. "Measuring manufacturing system complexity: a literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2865-2888, October.
    5. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).

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