IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-96-9697-0_71.html
   My bibliography  Save this book chapter

Optimization of Three-Dimensional Automated Warehouse Picking Energy Consumption Based on Simulated Annealing Algorithm

Author

Listed:
  • Yu Zhang

    (Beijing Jiaotong University)

  • Hua Yi

    (Beijing Jiaotong University)

Abstract

With the establishment of dual carbon goals, a new connotation is attributed to energy consumption control, demanding further clarification. In the process of picking operations, energy consumption is closely related to picking paths and the distribution of goods. Therefore, achieving efficient and rational allocation of storage locations, optimization of picking paths, and minimizing total energy consumption have become focal points for enterprises. Addressing these challenges, this paper, based on MATLAB programming, comprehensively considers factors including storage location coordinates, cargo weight, frequency of inbound and outbound operations, warehouse physical constraints, and practical constraints of picking tools. It constructs a location table and establishes an optimization model involving multiple types of goods and orders, considering energy consumption, within the context of real logistics warehouse picking operations. Utilizing the simulated annealing algorithm, we achieve the optimal picking paths for both conventional S-type picking and S-M-type picking, as well as optimize energy consumption through ABC classification of goods distribution.

Suggested Citation

  • Yu Zhang & Hua Yi, 2025. "Optimization of Three-Dimensional Automated Warehouse Picking Energy Consumption Based on Simulated Annealing Algorithm," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-981-96-9697-0_71
    DOI: 10.1007/978-981-96-9697-0_71
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:lnopch:978-981-96-9697-0_71. 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: 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.