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Part family grouping method for reconfigurable manufacturing system considering process time and capacity demand

Author

Listed:
  • Sihan Huang

    (Beijing Institute of Technology
    University of Michigan)

  • Yan Yan

    (Beijing Institute of Technology)

Abstract

Reconfigurable manufacturing system (RMS) is designed around part family providing exact production function and capacity in cost-effective way when needed. Besides the grouping accuracy of part family impacting the responsiveness of RMS, the efficiency problem of RMS resulting from the difference of process time and capacity demand should be solved. Therefore, a similarity coefficient method for RMS part family grouping considering process time and capacity demand is proposed. First, the longest common subsequence (LCS) among different part process routes is extracted and the shortest composite supersequence (SCS) of parts is constructed. Idle machine (IM) and bypass move (BPM) are analyzed based on SCS. Then, the process time (T) and capacity demand (D) are used as characteristic value of operation. And characteristic value sequences of process route, LCS, SCS, IM and BPM are gained, that is, TDP, TDLCS, TDSCS, TDIM and TDBPM respectively. By analyzing the relationships between TDLCS and TDSCS, the characteristic value sequences of TDLCS, TDIM and TDBPM are used to calculate the similarity between parts. Based on the similarity matrix, the netting clustering algorithm is used for clustering to complete the part family grouping. Finally, a case study is presented to implement the proposed method and validate the effectiveness.

Suggested Citation

  • Sihan Huang & Yan Yan, 2019. "Part family grouping method for reconfigurable manufacturing system considering process time and capacity demand," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 424-445, June.
    • Handle: RePEc:spr:flsman:v:31:y:2019:i:2:d:10.1007_s10696-018-9322-1
    DOI: 10.1007/s10696-018-9322-1
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    References listed on IDEAS

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    1. Yoram Koren & Wencai Wang & Xi Gu, 2017. "Value creation through design for scalability of reconfigurable manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1227-1242, March.
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    Cited by:

    1. Ateekh Ur Rehman & Syed Hammad Mian & Usama Umer & Yusuf Siraj Usmani, 2019. "Strategic Outcome Using Fuzzy-AHP-Based Decision Approach for Sustainable Manufacturing," Sustainability, MDPI, vol. 11(21), pages 1-22, October.
    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.

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