IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i4d10.1007_s10845-020-01596-9.html
   My bibliography  Save this article

Sequential graph-based routing algorithm for electrical harnesses, tubes, and hoses in a commercial vehicle

Author

Listed:
  • Saekyeol Kim

    (Hanyang University)

  • Taehyeok Choi

    (Hanyang University)

  • Shinyu Kim

    (Hanyang University)

  • Taejoon Kwon

    (Hanyang University)

  • Tae Hee Lee

    (Hanyang University)

  • Kwangrae Lee

    (Hyundai Motor Company)

Abstract

The routing design of the various electrical wires, tubes, and hoses of a commercial vehicle requires a significant number of man-hours because of the variety of the commercial vehicles, frequent design changes of other vehicular components and the manual trial-and-error approaches. This study proposes a new graph-based routing algorithm to find the collision-free routing path in the constrained space of a commercial vehicle. Minimal spanning tree is adopted to connect multi-terminal points in a graph and Dijkstra’s algorithm is used to find the shortest route among the candidate paths; the design domain is divided into several sub-domains to simplify the graph and the proposed algorithm solves the routing problems in a sequential manner to deal intermediate points. Then, the proposed method was applied to the design of the routes for four different routing components of a commercial truck. The results indicate that the developed methodology can provide a satisfactory routing design satisfying all the requirements of the design experts in the automotive industry.

Suggested Citation

  • Saekyeol Kim & Taehyeok Choi & Shinyu Kim & Taejoon Kwon & Tae Hee Lee & Kwangrae Lee, 2021. "Sequential graph-based routing algorithm for electrical harnesses, tubes, and hoses in a commercial vehicle," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 917-933, April.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01596-9
    DOI: 10.1007/s10845-020-01596-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01596-9
    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/s10845-020-01596-9?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. Yanfeng Qu & Dan Jiang & Qingyan Yang, 2018. "Branch pipe routing based on 3D connection graph and concurrent ant colony optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1647-1657, October.
    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. Xinjian Deng & Jianhua Liu & Hao Gong & Jiayu Huang, 2023. "A novel vision-based method for loosening detection of marked T-junction pipe fittings integrating GAN-based segmentation and SVM-based classification algorithms," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2581-2597, August.
    2. Qiaoyu Zhang & Yan Lin, 2024. "Integrating multi-agent reinforcement learning and 3D A* search for facility layout problem considering connector-assembly," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3393-3418, October.

    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. Xinjian Deng & Jianhua Liu & Hao Gong & Jiayu Huang, 2023. "A novel vision-based method for loosening detection of marked T-junction pipe fittings integrating GAN-based segmentation and SVM-based classification algorithms," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2581-2597, August.
    2. Qiaoyu Zhang & Yan Lin, 2024. "Integrating multi-agent reinforcement learning and 3D A* search for facility layout problem considering connector-assembly," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3393-3418, October.

    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:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01596-9. 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.