Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia
Designating the ideal shipping route can spare expenses, enlarge profits and improve the competitiveness of shipping companies. Liner shipping route choice is mainly contingent on fuel cost, which always contributes the major proportion of the ship's operating cost. Although many studies on thi...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-03-01
|
| Series: | Asian Journal of Shipping and Logistics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2092521220300249 |
| _version_ | 1818949680450502656 |
|---|---|
| author | Linh Bui-Duy Ngoc Vu-Thi-Minh |
| author_facet | Linh Bui-Duy Ngoc Vu-Thi-Minh |
| author_sort | Linh Bui-Duy |
| collection | DOAJ |
| description | Designating the ideal shipping route can spare expenses, enlarge profits and improve the competitiveness of shipping companies. Liner shipping route choice is mainly contingent on fuel cost, which always contributes the major proportion of the ship's operating cost. Although many studies on this topic have been carried out, none are based on the fuel consumption forecast model designed by the advanced machine learning method. This paper provides a platform idea for selecting the optimal operating route for container ships to minimize fuel cost by using an asymmetric traveling salesman problem (ATSP) algorithm solution, in which the fuel consumption model for the route is estimated based on the deep-machine learning method. Five input variables are given in the model including average velocity, sailing time, ship's capacity, wind speed, and wind direction. The mean absolute percentage error (MAPE) of the model is 5.89%, indicating that the predictive result obtains a very high accuracy, close to 95%. The optimal model is thus applied in combination with ATSP to address the optimal solution for a certain route. |
| first_indexed | 2024-12-20T09:06:33Z |
| format | Article |
| id | doaj.art-1317e59534b1457bac7ac782bf130a30 |
| institution | Directory Open Access Journal |
| issn | 2092-5212 |
| language | English |
| last_indexed | 2024-12-20T09:06:33Z |
| publishDate | 2021-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Asian Journal of Shipping and Logistics |
| spelling | doaj.art-1317e59534b1457bac7ac782bf130a302022-12-21T19:45:42ZengElsevierAsian Journal of Shipping and Logistics2092-52122021-03-01371111Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in AsiaLinh Bui-Duy0Ngoc Vu-Thi-Minh1School of Economics and International Business, Foreign Trade University, Hanoi, Viet NamCorresponding author.; School of Economics and International Business, Foreign Trade University, Hanoi, Viet NamDesignating the ideal shipping route can spare expenses, enlarge profits and improve the competitiveness of shipping companies. Liner shipping route choice is mainly contingent on fuel cost, which always contributes the major proportion of the ship's operating cost. Although many studies on this topic have been carried out, none are based on the fuel consumption forecast model designed by the advanced machine learning method. This paper provides a platform idea for selecting the optimal operating route for container ships to minimize fuel cost by using an asymmetric traveling salesman problem (ATSP) algorithm solution, in which the fuel consumption model for the route is estimated based on the deep-machine learning method. Five input variables are given in the model including average velocity, sailing time, ship's capacity, wind speed, and wind direction. The mean absolute percentage error (MAPE) of the model is 5.89%, indicating that the predictive result obtains a very high accuracy, close to 95%. The optimal model is thus applied in combination with ATSP to address the optimal solution for a certain route.http://www.sciencedirect.com/science/article/pii/S2092521220300249Asymmetric traveling salesman problemShip fuel consumption modelLiner shippingRoute choice optimizationDeep-learning neural networkContainer ships |
| spellingShingle | Linh Bui-Duy Ngoc Vu-Thi-Minh Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia Asian Journal of Shipping and Logistics Asymmetric traveling salesman problem Ship fuel consumption model Liner shipping Route choice optimization Deep-learning neural network Container ships |
| title | Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia |
| title_full | Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia |
| title_fullStr | Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia |
| title_full_unstemmed | Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia |
| title_short | Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia |
| title_sort | utilization of a deep learning based fuel consumption model in choosing a liner shipping route for container ships in asia |
| topic | Asymmetric traveling salesman problem Ship fuel consumption model Liner shipping Route choice optimization Deep-learning neural network Container ships |
| url | http://www.sciencedirect.com/science/article/pii/S2092521220300249 |
| work_keys_str_mv | AT linhbuiduy utilizationofadeeplearningbasedfuelconsumptionmodelinchoosingalinershippingrouteforcontainershipsinasia AT ngocvuthiminh utilizationofadeeplearningbasedfuelconsumptionmodelinchoosingalinershippingrouteforcontainershipsinasia |