IDEAS home Printed from https://ideas.repec.org/a/taf/tcybxx/v9y2023i2p174-192.html
   My bibliography  Save this article

A Improved Subgradient Lagrangian Relaxation Algorithm for Solving the Stochastic Demand Inventory Routing Problem

Author

Listed:
  • Yuan-Yuan Zhao
  • Qian-qian Duan

Abstract

In order to improve the coordination efficiency of vehicle routing problem, a multi-level stochastic demand in ventory routing problem was established in this paper, which minimizes the total cost of the system by determining the relation among inventory of distribution center, fleets, and customer needs. Solving the Lagrangian dual problem by the traditional subgradient Lagrangian relaxation algorithm may easily cause oscillation and then slow down the solving speed. To tackle the problem, an improved subgradient Lagrangian relaxation algorithm was proposed. Compared with the traditional subgradient algorithm , the proposed method is faster and improves the quality of the approximate solution.

Suggested Citation

  • Yuan-Yuan Zhao & Qian-qian Duan, 2023. "A Improved Subgradient Lagrangian Relaxation Algorithm for Solving the Stochastic Demand Inventory Routing Problem," Cyber-Physical Systems, Taylor & Francis Journals, vol. 9(2), pages 174-192, April.
    • Handle: RePEc:taf:tcybxx:v:9:y:2023:i:2:p:174-192
    DOI: 10.1080/23335777.2021.1946719
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23335777.2021.1946719
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23335777.2021.1946719?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.

    More about this item

    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:taf:tcybxx:v:9:y:2023:i:2:p:174-192. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tcyb .

    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.