IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v226y2020ics0925527320300037.html
   My bibliography  Save this article

Search algorithm of the assembly sequence of products by using past learning results

Author

Listed:
  • Watanabe, Keijiro
  • Inada, Shuhei

Abstract

In the future smart factory, the production system will further head in the direction of on-demand production. Products will be assembled one by one based on different specifications of customers. In these recognitions, this paper considers a method for raising productivity of the robot work cell. Under the assumption that the dual-arm robot assembles products in the work cell where one robot is in charge of all steps of assembling the product, we propose a computational algorithm for searching the efficient assembly sequence and work assignment to the robot hands utilizing reinforcement learning. Furthermore, we intend to use past learning results to determine work plans of robots more effectively. The proposed methods can eliminate or decrease the workload of the robot teaching. In addition, they can contribute to shorten the assembly time of products by giving the efficient work plan. In this research, the basic theory for automating the work planning of actual assembled products is considered using a building block model.

Suggested Citation

  • Watanabe, Keijiro & Inada, Shuhei, 2020. "Search algorithm of the assembly sequence of products by using past learning results," International Journal of Production Economics, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:proeco:v:226:y:2020:i:c:s0925527320300037
    DOI: 10.1016/j.ijpe.2020.107615
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527320300037
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2020.107615?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.

    Citations

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


    Cited by:

    1. Fatemi-Anaraki, Soroush & Tavakkoli-Moghaddam, Reza & Foumani, Mehdi & Vahedi-Nouri, Behdin, 2023. "Scheduling of Multi-Robot Job Shop Systems in Dynamic Environments: Mixed-Integer Linear Programming and Constraint Programming Approaches," Omega, Elsevier, vol. 115(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:eee:proeco:v:226:y:2020:i:c:s0925527320300037. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.