Container Liner Shipping System Design Considering Methanol-Powered Vessels
The transition from the use of heavy fuel oil (HFO) to the use of green fuels (e.g., methanol) for container liner shipping presents a significant challenge for liner shipping system design (LSSD) in terms of achieving emission reductions. While methanol, including both green and gray methanol, offe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/4/709 |
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| author | Zhaokun Li Xinke Yu Jianning Shang Kang Chen Xu Xin Wei Zhang Shaoqiang Yu |
| author_facet | Zhaokun Li Xinke Yu Jianning Shang Kang Chen Xu Xin Wei Zhang Shaoqiang Yu |
| author_sort | Zhaokun Li |
| collection | DOAJ |
| description | The transition from the use of heavy fuel oil (HFO) to the use of green fuels (e.g., methanol) for container liner shipping presents a significant challenge for liner shipping system design (LSSD) in terms of achieving emission reductions. While methanol, including both green and gray methanol, offers environmental benefits, its lower energy density introduces operational complexities. Motivated by the aforementioned background, we establish a bi-level programming model. This model integrates liner speed management and bunker fuel management strategies (i.e., bunkering port selection and bunkering amount determination) with traditional network design decision (i.e., fleet deployment, shipping network design, and slot allocation) optimization. Specifically, the upper-level model optimizes the number of liners deployed in the fleet and shipping network structure, whereas the lower-level model coordinates decisions associated with liner sailing speed management, bunker fuel management, and slot allocation. Moreover, we propose an adaptive piecewise linearization approach combined with a genetic algorithm, which can efficiently solve large-scale instances. Sensitivity analyses of fuel types and fuel prices are conducted to demonstrate the effectiveness of the model and algorithm. Overall, our paper offers valuable insights for policymakers in designing customized emission reduction policies to support the green fuel transition in the maritime industry. |
| format | Article |
| id | doaj-art-3c27b4d8a2344d40b99b1e8a336caeac |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-3c27b4d8a2344d40b99b1e8a336caeac2025-08-20T03:13:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-04-0113470910.3390/jmse13040709Container Liner Shipping System Design Considering Methanol-Powered VesselsZhaokun Li0Xinke Yu1Jianning Shang2Kang Chen3Xu Xin4Wei Zhang5Shaoqiang Yu6School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaDepartment of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 999077, ChinaCentre for Maritime and Logistics Management, Australian Maritime College, University of Tasmania, Maritime Way, Newnham, TAS 7248, AustraliaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaThe transition from the use of heavy fuel oil (HFO) to the use of green fuels (e.g., methanol) for container liner shipping presents a significant challenge for liner shipping system design (LSSD) in terms of achieving emission reductions. While methanol, including both green and gray methanol, offers environmental benefits, its lower energy density introduces operational complexities. Motivated by the aforementioned background, we establish a bi-level programming model. This model integrates liner speed management and bunker fuel management strategies (i.e., bunkering port selection and bunkering amount determination) with traditional network design decision (i.e., fleet deployment, shipping network design, and slot allocation) optimization. Specifically, the upper-level model optimizes the number of liners deployed in the fleet and shipping network structure, whereas the lower-level model coordinates decisions associated with liner sailing speed management, bunker fuel management, and slot allocation. Moreover, we propose an adaptive piecewise linearization approach combined with a genetic algorithm, which can efficiently solve large-scale instances. Sensitivity analyses of fuel types and fuel prices are conducted to demonstrate the effectiveness of the model and algorithm. Overall, our paper offers valuable insights for policymakers in designing customized emission reduction policies to support the green fuel transition in the maritime industry.https://www.mdpi.com/2077-1312/13/4/709liner shipping network designbunker fuel managementgreen methanolbi-level programminggenetic algorithm |
| spellingShingle | Zhaokun Li Xinke Yu Jianning Shang Kang Chen Xu Xin Wei Zhang Shaoqiang Yu Container Liner Shipping System Design Considering Methanol-Powered Vessels Journal of Marine Science and Engineering liner shipping network design bunker fuel management green methanol bi-level programming genetic algorithm |
| title | Container Liner Shipping System Design Considering Methanol-Powered Vessels |
| title_full | Container Liner Shipping System Design Considering Methanol-Powered Vessels |
| title_fullStr | Container Liner Shipping System Design Considering Methanol-Powered Vessels |
| title_full_unstemmed | Container Liner Shipping System Design Considering Methanol-Powered Vessels |
| title_short | Container Liner Shipping System Design Considering Methanol-Powered Vessels |
| title_sort | container liner shipping system design considering methanol powered vessels |
| topic | liner shipping network design bunker fuel management green methanol bi-level programming genetic algorithm |
| url | https://www.mdpi.com/2077-1312/13/4/709 |
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