<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

A Regression Model of Dry Bulk Carriers’ Loading Time

University Adriatic, Faculty of Maritime Studies

LOGI – Scientific Journal on Transport and Logistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Regression Model of Dry Bulk Carriers\u2019 Loading Time"}

Although optimization of Vessel Turnaround Time (VTT) is a well-known research problem and its importance has long been understood, research on dry bulk...

A Regression Model of Dry Bulk Carriers’ Loading Time

Publié en ligne: 29 avr. 2024

Pages: 49 - 60

Reçu: 05 déc. 2023

Accepté: 17 mars 2024

DOI: https://doi.org/10.2478/logi-2024-0005

Mots clés
Dry bulk carrier, Vessel Turnaround Time, loading time, regression model

© 2024 Deda Đelović et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Regression Model of Dry Bulk Carriers’ Loading Time

Publié en ligne: 29 avr. 2024

Pages: 49 - 60

Reçu: 05 déc. 2023

Accepté: 17 mars 2024

DOI: https://doi.org/10.2478/logi-2024-0005

Mots clésDry bulk carrier, Vessel Turnaround Time, loading time, regression model

© 2024 Deda Đelović et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Dry bulk carrier, Vessel Turnaround Time, loading time, regression model