IDEAS home Printed from https://ideas.repec.org/a/spr/flsman/v31y2019i2d10.1007_s10696-018-9322-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10696-018-9322-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10696-018-9322-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huan Shao & Aiping Li & Liyun Xu & Giovanni Moroni, 2019. "Scalability in manufacturing systems: a hybridized GA approach," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1859-1879, April.
    2. Hosseini, Amir & Otto, Alena & Pesch, Erwin, 2024. "Scheduling in manufacturing with transportation: Classification and solution techniques," European Journal of Operational Research, Elsevier, vol. 315(3), pages 821-843.
    3. Youssef Lahrichi & Laurent Deroussi & Nathalie Grangeon & Sylvie Norre, 2021. "A balance-first sequence-last algorithm to design RMS: a matheuristic with performance guaranty to balance reconfigurable manufacturing systems," Journal of Heuristics, Springer, vol. 27(1), pages 107-132, April.
    4. Bustinza, Oscar F. & Opazo-Basaez, Marco & Tarba, Shlomo, 2022. "Exploring the interplay between Smart Manufacturing and KIBS firms in configuring product-service innovation performance," Technovation, Elsevier, vol. 118(C).
    5. Carlos Alberto Barrera-Diaz & Amir Nourmohammadi & Henrik Smedberg & Tehseen Aslam & Amos H. C. Ng, 2023. "An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems," Mathematics, MDPI, vol. 11(6), pages 1-23, March.
    6. Delorme, Xavier & Cerqueus, Audrey & Gianessi, Paolo & Lamy, Damien, 2023. "RMS balancing and planning under uncertain demand and energy cost considerations," International Journal of Production Economics, Elsevier, vol. 261(C).
    7. Marcello Colledani & Anteneh Yemane & Giovanni Lugaresi & Giovanni Borzi & Daniele Callegaro, 2018. "A software platform for supporting the design and reconfiguration of versatile assembly systems," Post-Print hal-03880599, HAL.
    8. Ortega-Jimenez, Cesar H. & Garrido-Vega, Pedro & Cruz Torres, Cristian Andrés, 2020. "Achieving plant responsiveness from reconfigurable technology: Intervening role of SCM," International Journal of Production Economics, Elsevier, vol. 219(C), pages 195-203.
    9. Weckenborg, Christian & Schumacher, Patrick & Thies, Christian & Spengler, Thomas S., 2024. "Flexibility in manufacturing system design: A review of recent approaches from Operations Research," European Journal of Operational Research, Elsevier, vol. 315(2), pages 413-441.
    10. 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).
    11. Bashir Salah & Mustufa Haider Abidi & Syed Hammad Mian & Mohammed Krid & Hisham Alkhalefah & Ali Abdo, 2019. "Virtual Reality-Based Engineering Education to Enhance Manufacturing Sustainability in Industry 4.0," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    12. Battaïa, Olga & Dolgui, Alexandre & Guschinsky, Nikolai, 2023. "MIP-based heuristics for combinatorial design of reconfigurable rotary transfer machines for production of multiple parts," International Journal of Production Economics, Elsevier, vol. 262(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:flsman:v:31:y:2019:i:2:d:10.1007_s10696-018-9322-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.