IDEAS home Printed from https://ideas.repec.org/a/wsi/apjorx/v37y2020i05ns0217595920500189.html
   My bibliography  Save this article

A Decision Support System for Data-Driven Driver-Experience Augmented Vehicle Routing Problem

Author

Listed:
  • Qitong Zhao

    (School of Business, Singapore University of Social Sciences, 463 Clementi Rd, Singapore University of Social Sciences, Singapore 599494, Singapore)

  • Chenhao Zhou

    (Department of Industrial Systems Engineering and Management, National University of Singapore, 3 Research Link, Innovation 4.0, University of Singapore, Singapore 117602, Singapore)

  • Giulia Pedrielli

    (School of Computing Informatics and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ 85281, USA)

Abstract

Logistics delivery companies typically deal with delivery problems that are strictly constrained by time while ensuring optimality of the solution to remain competitive. Often, the companies depend on intuition and experience of the planners and couriers in their daily operations. Therefore, despite the variability-characterizing daily deliveries, the number of vehicles used every day are relatively constant. This motivates us towards reducing the operational variable costs by proposing an efficient heuristic that improves on the clustering and routing phases. In this paper, a decision support system (DSS) and the corresponding clustering and routing methodology are presented, incorporating the driver’s experience, the company’s historical data and Google map’s data. The proposed heuristic performs as well as k-means algorithm while having other notable advantages. The superiority of the proposed approach has been illustrated through numerical examples.

Suggested Citation

  • Qitong Zhao & Chenhao Zhou & Giulia Pedrielli, 2020. "A Decision Support System for Data-Driven Driver-Experience Augmented Vehicle Routing Problem," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 37(05), pages 1-23, October.
    • Handle: RePEc:wsi:apjorx:v:37:y:2020:i:05:n:s0217595920500189
    DOI: 10.1142/S0217595920500189
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0217595920500189
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0217595920500189?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.

    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:wsi:apjorx:v:37:y:2020:i:05:n:s0217595920500189. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/apjor/apjor.shtml .

    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.