IDEAS home Printed from https://ideas.repec.org/a/vrs/logitl/v15y2024i1p49-60n5.html
   My bibliography  Save this article

A Regression Model of Dry Bulk Carriers’ Loading Time

Author

Listed:
  • Đelović Deda

    (1 University Adriatic, Faculty of Maritime Studies, Setaliste Kralja Nikole, Potkovica D1, Bar, and Port of Bar JSC, Obala 13. jula 2, Bar, Montenegro)

Abstract

Although optimization of Vessel Turnaround Time (VTT) is a well-known research problem and its importance has long been understood, research on dry bulk carriers VTT has no priority in the available literature. It is one of the initial motives of the author to write this paper. After a general theoretical introduction, this paper presents research on dry bulk carriers’ loading time, as a component with the dominant share in the total VTT. Through the research, a mathematical model of interdependencies between dry bulk carrier‘s loading time and selected independent variables is defined using a multiple linear regression model. Results of statistical significance tests confirmed that the best-fit regression model is the one which adequately describes the correlation between the dry bulk carrier‘s loading time and the following independent variables: cargo quantity (loaded), number of used cranes per vessel and loading process interruptions. The presented results establish important bases for the author’s further research in this field as well as reliable planning bases for cargo handling management processes at dry bulk cargo terminals where cargo loading/unloading to/from vessels is realized by gantry cranes and/or mobile harbour cranes.

Suggested Citation

  • Đelović Deda, 2024. "A Regression Model of Dry Bulk Carriers’ Loading Time," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 15(1), pages 49-60, January.
  • Handle: RePEc:vrs:logitl:v:15:y:2024:i:1:p:49-60:n:5
    DOI: 10.2478/logi-2024-0005
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/logi-2024-0005
    Download Restriction: no

    File URL: https://libkey.io/10.2478/logi-2024-0005?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
    ---><---

    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:vrs:logitl:v:15:y:2024:i:1:p:49-60:n:5. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.