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

Author

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  • Tsai, Chieh-Yuan
  • Tsai, Chi-Yang
  • Huang, Po-Wen

Abstract

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.

Suggested Citation

  • Tsai, Chieh-Yuan & Tsai, Chi-Yang & Huang, Po-Wen, 2009. "An association clustering algorithm for can-order policies in the joint replenishment problem," International Journal of Production Economics, Elsevier, vol. 117(1), pages 30-41, January.
  • Handle: RePEc:eee:proeco:v:117:y:2009:i:1:p:30-41
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    References listed on IDEAS

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    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.
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    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.
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    Cited by:

    1. Anzanello, Michel J. & Fogliatto, Flavio S., 2011. "Selecting the best clustering variables for grouping mass-customized products involving workers' learning," International Journal of Production Economics, Elsevier, vol. 130(2), pages 268-276, April.
    2. Ting, S.L. & Tse, Y.K. & Ho, G.T.S. & Chung, S.H. & Pang, G., 2014. "Mining logistics data to assure the quality in a sustainable food supply chain: A case in the red wine industry," International Journal of Production Economics, Elsevier, vol. 152(C), pages 200-209.
    3. repec:wsi:apjorx:v:34:y:2017:i:04:n:s0217595917500166 is not listed on IDEAS
    4. Lau, H.C.W. & Ho, G.T.S. & Zhao, Y. & Chung, N.S.H., 2009. "Development of a process mining system for supporting knowledge discovery in a supply chain network," International Journal of Production Economics, Elsevier, vol. 122(1), pages 176-187, November.

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