IDEAS home Printed from https://ideas.repec.org/a/ids/ijores/v28y2017i4p506-527.html
   My bibliography  Save this article

A hybrid search approach in production-distribution planning problem in supply chain using multi-agent systems

Author

Listed:
  • Abolfazl Kazemi
  • Mohammad Hossein Fazel Zarandi
  • Mahdi Azizmohammadi

Abstract

The production-distribution planning is one of the most important approaches to support global optimisation in supply chain management (SCM), and should be solved within the integrated structure. The production-distribution planning problem (PDPP) involves the determination of the best configuration regarding location, size, technology content and product range to achieve the firm's long-term goals. On the other hand, teams of autonomous agents (ATeams), cooperating by sharing solutions through a common memory, have been proposed as a means of solving combinatorial optimisation problems. In this paper a hybrid search approach is presented using an agent-based system by considering ATeams concept for solving the PDPP. For this purpose, three algorithms are provided to solve the PDPP: genetic algorithm (GA), tabu search (TS) and simulated annealing (SA). Then we combine these algorithms using a multi-agent system and an integrated solution algorithm is proposed. Finally, the proposed approach is compared against LINGO software. The obtained results reveal that the use of multi-agent system delivers better solutions to us.

Suggested Citation

  • Abolfazl Kazemi & Mohammad Hossein Fazel Zarandi & Mahdi Azizmohammadi, 2017. "A hybrid search approach in production-distribution planning problem in supply chain using multi-agent systems," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 28(4), pages 506-527.
    • Handle: RePEc:ids:ijores:v:28:y:2017:i:4:p:506-527
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=82611
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijores:v:28:y:2017:i:4:p:506-527. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=170 .

    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.