Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework
This study addresses the integrated scheduling problem of dry bulk cargo terminal yards, which includes three components: transportation planning, yard selection optimization, and equipment scheduling. Additionally, the research integrates safety considerations and addresses the complexities of dyna...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/1/105 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588238226718720 |
|---|---|
| author | Haijiang Li Jiapeng Zhao Peng Jia Hongdong Ou Weili Zhao |
| author_facet | Haijiang Li Jiapeng Zhao Peng Jia Hongdong Ou Weili Zhao |
| author_sort | Haijiang Li |
| collection | DOAJ |
| description | This study addresses the integrated scheduling problem of dry bulk cargo terminal yards, which includes three components: transportation planning, yard selection optimization, and equipment scheduling. Additionally, the research integrates safety considerations and addresses the complexities of dynamic transportation planning. This work presents two innovations. Firstly, this study develops a sophisticated modeling framework that integrates graph structures for precise yard mapping with mixed-integer programming to enforce operational constraints. This integrated approach facilitates a more accurate and comprehensive representation of yard operations, capturing diverse operational aspects while maintaining model clarity and computational efficiency. Secondly, this study proposes an advanced solution methodology that employs a reinforcement learning technique integrating a Dueling Deep Q-Network and Double Deep Q-Network. This hybrid algorithm significantly enhances optimization performance and accelerates the learning process, thereby improving the efficiency of the solutions. The experimental results demonstrate that the proposed model effectively manages the integrated scheduling of bulk material ingress, storage, and egress within the yard. The operational plans generated by the approach outperform traditional first-come, first-served strategies, showcasing substantial improvements in port operational efficiency and reliability. This comprehensive solution underscores the potential for significant advancements in the overall management and performance of dry bulk cargo ports. |
| format | Article |
| id | doaj-art-df8c63ac3980451499e636d431d397e5 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-df8c63ac3980451499e636d431d397e52025-01-24T13:36:52ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113110510.3390/jmse13010105Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning FrameworkHaijiang Li0Jiapeng Zhao1Peng Jia2Hongdong Ou3Weili Zhao4School 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, ChinaQingdao Port International Company Limited Qiangang Branch, Qingdao 266011, ChinaThis study addresses the integrated scheduling problem of dry bulk cargo terminal yards, which includes three components: transportation planning, yard selection optimization, and equipment scheduling. Additionally, the research integrates safety considerations and addresses the complexities of dynamic transportation planning. This work presents two innovations. Firstly, this study develops a sophisticated modeling framework that integrates graph structures for precise yard mapping with mixed-integer programming to enforce operational constraints. This integrated approach facilitates a more accurate and comprehensive representation of yard operations, capturing diverse operational aspects while maintaining model clarity and computational efficiency. Secondly, this study proposes an advanced solution methodology that employs a reinforcement learning technique integrating a Dueling Deep Q-Network and Double Deep Q-Network. This hybrid algorithm significantly enhances optimization performance and accelerates the learning process, thereby improving the efficiency of the solutions. The experimental results demonstrate that the proposed model effectively manages the integrated scheduling of bulk material ingress, storage, and egress within the yard. The operational plans generated by the approach outperform traditional first-come, first-served strategies, showcasing substantial improvements in port operational efficiency and reliability. This comprehensive solution underscores the potential for significant advancements in the overall management and performance of dry bulk cargo ports.https://www.mdpi.com/2077-1312/13/1/105deep reinforcement learningdry bulk freight yardmixed-integer programmingoperational process planning |
| spellingShingle | Haijiang Li Jiapeng Zhao Peng Jia Hongdong Ou Weili Zhao Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework Journal of Marine Science and Engineering deep reinforcement learning dry bulk freight yard mixed-integer programming operational process planning |
| title | Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework |
| title_full | Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework |
| title_fullStr | Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework |
| title_full_unstemmed | Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework |
| title_short | Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework |
| title_sort | optimization of bulk cargo terminal unloading and outbound operations based on a deep reinforcement learning framework |
| topic | deep reinforcement learning dry bulk freight yard mixed-integer programming operational process planning |
| url | https://www.mdpi.com/2077-1312/13/1/105 |
| work_keys_str_mv | AT haijiangli optimizationofbulkcargoterminalunloadingandoutboundoperationsbasedonadeepreinforcementlearningframework AT jiapengzhao optimizationofbulkcargoterminalunloadingandoutboundoperationsbasedonadeepreinforcementlearningframework AT pengjia optimizationofbulkcargoterminalunloadingandoutboundoperationsbasedonadeepreinforcementlearningframework AT hongdongou optimizationofbulkcargoterminalunloadingandoutboundoperationsbasedonadeepreinforcementlearningframework AT weilizhao optimizationofbulkcargoterminalunloadingandoutboundoperationsbasedonadeepreinforcementlearningframework |