IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

An association clustering algorithm for can-order policies in the joint replenishment problem

Listed author(s):
  • Tsai, Chieh-Yuan
  • Tsai, Chi-Yang
  • Huang, Po-Wen
Registered author(s):

    Many studies have shown that the total cost of employing joint replenishment for correlated items is less than the total cost of using single-item replenishment. Savings increase dramatically when the demand between items is closely related. Although the benefits of joint replenishment are significant, it is difficult to define the demand correlation among items, especially when the number of items increases. A large number of items reduces the efficiency and advantage of the multi-item inventory control. To overcome this difficulty, an association clustering algorithm this paper proposes to evaluate the correlated demands among items. The proposed algorithm utilizes the "support" concept in association rule analysis to measure the similarity among items. Based on these measurements a clustering method is developed to group items with close demand in a hierarchal way. The can-order policy is then applied to the optimal clustering result as decided by the proposed performance index. To illustrate the benefits of the proposed association clustering algorithm for replenishment systems, a set of simulations and a sensitivity analysis is conducted. The results of the experiments show that the proposed method outperforms several replenishment models.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 117 (2009)
    Issue (Month): 1 (January)
    Pages: 30-41

    in new window

    Handle: RePEc:eee:proeco:v:117:y:2009:i:1:p:30-41
    Contact details of provider: Web page:

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. Melchiors, Philip, 2002. "Calculating can-order policies for the joint replenishment problem by the compensation approach," European Journal of Operational Research, Elsevier, vol. 141(3), pages 587-595, September.
    2. Joseph L. Balintfy, 1964. "On a Basic Class of Multi-Item Inventory Problems," Management Science, INFORMS, vol. 10(2), pages 287-297, January.
    3. Dellaert, Nico & van de Poel, Erik, 1996. "Global inventory control in an academic hospital," International Journal of Production Economics, Elsevier, vol. 46(1), pages 277-284, December.
    4. Derek R. Atkins & Paul O. Iyogun, 1988. "Periodic Versus "Can-Order" Policies for Coordinated Multi-Item Inventory Systems," Management Science, INFORMS, vol. 34(6), pages 791-796, June.
    5. Edward Ignall, 1969. "Optimal Continuous Review Policies for Two Product Inventory Systems with Joint Setup Costs," Management Science, INFORMS, vol. 15(5), pages 278-283, January.
    6. Liu, Liming & Yuan, Xue-Ming, 2000. "Coordinated replenishments in inventory systems with correlated demands," European Journal of Operational Research, Elsevier, vol. 123(3), pages 490-503, June.
    7. Olsen, Anne L., 2008. "Inventory replenishment with interdependent ordering costs: An evolutionary algorithm solution," International Journal of Production Economics, Elsevier, vol. 113(1), pages 359-369, May.
    8. Nilsson, Andreas & Segerstedt, Anders & van der Sluis, Erik, 2007. "A new iterative heuristic to solve the joint replenishment problem using a spreadsheet technique," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 399-405, July.
    9. Porras, Eric & Dekker, Rommert, 2008. "A solution method for the joint replenishment problem with correction factor," International Journal of Production Economics, Elsevier, vol. 113(2), pages 834-851, June.
    10. A. Federgruen & H. Groenevelt & H. C. Tijms, 1984. "Coordinated Replenishments in a Multi-Item Inventory System with Compound Poisson Demands," Management Science, INFORMS, vol. 30(3), pages 344-357, March.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:117:y:2009:i:1:p:30-41. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.