Assessing the factors impacting shipping container dwell time: a multi-port optimization study
Ocean transportation is the most preferred mode of transportation that represents a significant role in the global trade. Ocean transportation comprises around 80% of the aggregate worldwide cargo volume. This research paper focused on evaluating the factors that influence the dwell time of the shi...
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2024-02-01
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| Series: | Business: Theory and Practice |
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| Online Access: | https://jeelm.vgtu.lt/index.php/BTP/article/view/19205 |
| _version_ | 1827363255531601920 |
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| author | Mohan Saini Tone Lerher |
| author_facet | Mohan Saini Tone Lerher |
| author_sort | Mohan Saini |
| collection | DOAJ |
| description |
Ocean transportation is the most preferred mode of transportation that represents a significant role in the global trade. Ocean transportation comprises around 80% of the aggregate worldwide cargo volume. This research paper focused on evaluating the factors that influence the dwell time of the shipping containers. Dwell time is one of the important port performance parameters which evaluates the time spent by the container in a port. In this research, the data from the fourteen major ports was collected and analysed across the variables, such as cycle, size, mode, status, delivery and tracking technology for evaluating the variation in container dwell time. OLS regression method (Ordinary least squares) along with independent sample T test was adopted for the analysis of 2.8 million container data entries utilizing python for big data analysis and SPSS. For the top three ports with lowest RMSE (Root mean square error), Port A – 15.6 %, Port G – 15.7 % and Port L – 15.86 %, a qualitative study was performed to identify the reasons for the variation in dwell time. The major reasons identified included free days period, trans-shipment port, high rail frequency, industrial hubs in the vicinity of the ports for lower dwell time. A qualitative research framework was presented as the research outcomes and reasons for variations in a multiport study.
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| first_indexed | 2024-03-08T07:45:23Z |
| format | Article |
| id | doaj.art-ef7a2bccc4884de08f305b627cba7891 |
| institution | Directory Open Access Journal |
| issn | 1648-0627 1822-4202 |
| language | English |
| last_indexed | 2024-03-08T07:45:23Z |
| publishDate | 2024-02-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Business: Theory and Practice |
| spelling | doaj.art-ef7a2bccc4884de08f305b627cba78912024-02-02T16:23:46ZengVilnius Gediminas Technical UniversityBusiness: Theory and Practice1648-06271822-42022024-02-0125110.3846/btp.2024.19205Assessing the factors impacting shipping container dwell time: a multi-port optimization studyMohan Saini0Tone Lerher1Department of Industrial Engineering and Information Systems, Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic; School of Management and Commerce, Dev Bhoomi Uttarakhand University, Dehradun, Uttarakhand, India Faculty of Logistics, Laboratory for Cognitive Systems in Logistics, University of Maribor, Celje, Slovenia Ocean transportation is the most preferred mode of transportation that represents a significant role in the global trade. Ocean transportation comprises around 80% of the aggregate worldwide cargo volume. This research paper focused on evaluating the factors that influence the dwell time of the shipping containers. Dwell time is one of the important port performance parameters which evaluates the time spent by the container in a port. In this research, the data from the fourteen major ports was collected and analysed across the variables, such as cycle, size, mode, status, delivery and tracking technology for evaluating the variation in container dwell time. OLS regression method (Ordinary least squares) along with independent sample T test was adopted for the analysis of 2.8 million container data entries utilizing python for big data analysis and SPSS. For the top three ports with lowest RMSE (Root mean square error), Port A – 15.6 %, Port G – 15.7 % and Port L – 15.86 %, a qualitative study was performed to identify the reasons for the variation in dwell time. The major reasons identified included free days period, trans-shipment port, high rail frequency, industrial hubs in the vicinity of the ports for lower dwell time. A qualitative research framework was presented as the research outcomes and reasons for variations in a multiport study. https://jeelm.vgtu.lt/index.php/BTP/article/view/19205dwell timeport performanceoptimizationcontainershippingocean port |
| spellingShingle | Mohan Saini Tone Lerher Assessing the factors impacting shipping container dwell time: a multi-port optimization study Business: Theory and Practice dwell time port performance optimization container shipping ocean port |
| title | Assessing the factors impacting shipping container dwell time: a multi-port optimization study |
| title_full | Assessing the factors impacting shipping container dwell time: a multi-port optimization study |
| title_fullStr | Assessing the factors impacting shipping container dwell time: a multi-port optimization study |
| title_full_unstemmed | Assessing the factors impacting shipping container dwell time: a multi-port optimization study |
| title_short | Assessing the factors impacting shipping container dwell time: a multi-port optimization study |
| title_sort | assessing the factors impacting shipping container dwell time a multi port optimization study |
| topic | dwell time port performance optimization container shipping ocean port |
| url | https://jeelm.vgtu.lt/index.php/BTP/article/view/19205 |
| work_keys_str_mv | AT mohansaini assessingthefactorsimpactingshippingcontainerdwelltimeamultiportoptimizationstudy AT tonelerher assessingthefactorsimpactingshippingcontainerdwelltimeamultiportoptimizationstudy |