IDEAS home Printed from https://ideas.repec.org/a/ids/ijlsma/v43y2022i2p238-267.html
   My bibliography  Save this article

A non-stationary queuing approach and genetic algorithm for the optimisation of truck appointment system in a container terminal in Casablanca City

Author

Listed:
  • Sara Belaqziz
  • Fatima Bouyahia
  • Saâd Lissane Elhaq
  • Jaouad Boukachour

Abstract

Due to the increasing container traffic, many terminals face a considerable number of truck arrivals. This situation leads to congestion problems at the gates and generates serious air pollution while decreasing terminal efficiency. To consider this issue, many terminals use a truck appointment system. However, the latter should take, necessarily, into account the terminal's local conditions to ensure a satisfying performance. In the present work, one proposes an appointment model to control truck arrivals in one of the busiest terminals in Morocco. The model is based on an improvement of the approximation approach related to the queue length estimation. The treatment adopts a genetic algorithm with a novel testing scenarios highlighting more the solution performances by crossing several basic existing scenarios. Then, some numerical experiments are conducted based on literature works data to calibrate the model and ensure its accuracy. Finally, the best configuration was approved for the local terminal.

Suggested Citation

  • Sara Belaqziz & Fatima Bouyahia & Saâd Lissane Elhaq & Jaouad Boukachour, 2022. "A non-stationary queuing approach and genetic algorithm for the optimisation of truck appointment system in a container terminal in Casablanca City," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 43(2), pages 238-267.
  • Handle: RePEc:ids:ijlsma:v:43:y:2022:i:2:p:238-267
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=126017
    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:ijlsma:v:43:y:2022:i:2:p:238-267. 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=134 .

    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.