IDEAS home Printed from https://ideas.repec.org/a/bpj/jossai/v3y2015i1p25-36n3.html
   My bibliography  Save this article

A Dynamic Clustering Method to Large-Scale Distribution Problems

Author

Listed:
  • Tang Zhizhong
  • Li Bo
  • Qiu Hongyan

    (College of Management and Economics, Tianjin University, Tianjin300072, China)

Abstract

This paper presents the dynamic fuzzy clustering method to solve the multi-producers to multi-customers large-scale distribution problem. The proposed method includes three phases: Static clustering, order processing, and dynamic clustering. Based on the distances among customers, k-means method is used to generate the static clusters. The service priorities of each producer serving the static customer groups are ranked according to the distance performance. In the case of fluctuant customer orders, order processing can divide customer orders into several consecutive periods. After the above two phases, the fuzzy clustering technique is applied to further conduct dynamic clustering based on the customer order attributes. Similarly, the service priorities of generated dynamic customer groups will be ranked according to the time attributes of orders. Finally, by the real case, the authors obtain the conclusion that using the proposed method, the total cost of the producer is reduced by about 35%, and the vehicle loading rates are almost above 95%.

Suggested Citation

  • Tang Zhizhong & Li Bo & Qiu Hongyan, 2015. "A Dynamic Clustering Method to Large-Scale Distribution Problems," Journal of Systems Science and Information, De Gruyter, vol. 3(1), pages 25-36, February.
    • Handle: RePEc:bpj:jossai:v:3:y:2015:i:1:p:25-36:n:3
    DOI: 10.1515/JSSI-2015-0025
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/JSSI-2015-0025
    Download Restriction: no
    File URL: https://libkey.io/10.1515/JSSI-2015-0025?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
    ---><---

    References listed on IDEAS

    as
    1. Sheu, Jiuh-Biing, 2006. "A novel dynamic resource allocation model for demand-responsive city logistics distribution operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 42(6), pages 445-472, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ngo, Huan Hoang & Shah, Rohan & Mishra, Sabyasachee, 2018. "Optimal asset management strategies for mixed transit fleet," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 103-116.
    2. Niu, Yi-Feng & Gao, Zi-You & Lam, William H.K., 2017. "Evaluating the reliability of a stochastic distribution network in terms of minimal cuts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 75-97.
    3. Mathew, Tom V. & Khasnabis, Snehamay & Mishra, Sabyasachee, 2010. "Optimal resource allocation among transit agencies for fleet management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 418-432, July.
    4. Jidong Guo & Qiuhong Zhao & Menghao Xi, 2022. "Sustainable Urban Logistics Distribution Network Planning with Carbon Tax," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    5. Mishra, Sabyasachee & Sharma, Sushant & Khasnabis, Snehamay & Mathew, Tom V., 2013. "Preserving an aging transit fleet: An optimal resource allocation perspective based on service life and constrained budget," Transportation Research Part A: Policy and Practice, Elsevier, vol. 47(C), pages 111-123.

    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:bpj:jossai:v:3:y:2015:i:1:p:25-36:n:3. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc 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 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.