Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement Learning
Maritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The current first-come-first-serve method is insufficie...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/5/1025 |
| _version_ | 1797599551150882816 |
|---|---|
| author | Yonggai Dai Zongchen Li Boyu Wang |
| author_facet | Yonggai Dai Zongchen Li Boyu Wang |
| author_sort | Yonggai Dai |
| collection | DOAJ |
| description | Maritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The current first-come-first-serve method is insufficient in maintaining operational efficiency, especially under complicated conditions such as parallel scheduling with different cargo setups. In addition, in the face of rising demand, data-driven strategies are necessary. To tackle this issue, this paper proposes a mixed-integer model for allocating quay cranes, terminals, and berths. It considers not only cargo types, but also the time required for a quay crane setup. The proposed model features a greedy-insert-based offline algorithm that optimizes berth allocation when vessel information is available. In situations where vessel information is uncertain, the model utilizes an online optimization strategy based on a reinforcement-learning algorithm that is capable of learning from feedback and of adapting quickly in real time. The results of the numerical experiments demonstrate that both the offline and online algorithms can significantly enhance cargo handling efficiency and overall harbor operation. Furthermore, they have the potential to be extended to other complex settings. |
| first_indexed | 2024-03-11T03:36:51Z |
| format | Article |
| id | doaj.art-d23ad814a1824dca86a1802523c98a9e |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T03:36:51Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-d23ad814a1824dca86a1802523c98a9e2023-11-18T02:00:11ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-05-01115102510.3390/jmse11051025Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement LearningYonggai Dai0Zongchen Li1Boyu Wang2Wuhan Hangke Logistics Company Limited, CCCC Second Harbor Engineering Company Ltd., Wuhan 430013, ChinaMechanical Systems Engineering, EMPA-Swiss Federal Laboratories for Materials Science and Technology, 8600 Duebendorf, SwitzerlandShenzhen CaiGao Tech, Shenzhen 518067, ChinaMaritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The current first-come-first-serve method is insufficient in maintaining operational efficiency, especially under complicated conditions such as parallel scheduling with different cargo setups. In addition, in the face of rising demand, data-driven strategies are necessary. To tackle this issue, this paper proposes a mixed-integer model for allocating quay cranes, terminals, and berths. It considers not only cargo types, but also the time required for a quay crane setup. The proposed model features a greedy-insert-based offline algorithm that optimizes berth allocation when vessel information is available. In situations where vessel information is uncertain, the model utilizes an online optimization strategy based on a reinforcement-learning algorithm that is capable of learning from feedback and of adapting quickly in real time. The results of the numerical experiments demonstrate that both the offline and online algorithms can significantly enhance cargo handling efficiency and overall harbor operation. Furthermore, they have the potential to be extended to other complex settings.https://www.mdpi.com/2077-1312/11/5/1025berth allocationonline optimizationreinforcement learningquay crane setup |
| spellingShingle | Yonggai Dai Zongchen Li Boyu Wang Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement Learning Journal of Marine Science and Engineering berth allocation online optimization reinforcement learning quay crane setup |
| title | Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement Learning |
| title_full | Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement Learning |
| title_fullStr | Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement Learning |
| title_full_unstemmed | Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement Learning |
| title_short | Optimizing Berth Allocation in Maritime Transportation with Quay Crane Setup Times Using Reinforcement Learning |
| title_sort | optimizing berth allocation in maritime transportation with quay crane setup times using reinforcement learning |
| topic | berth allocation online optimization reinforcement learning quay crane setup |
| url | https://www.mdpi.com/2077-1312/11/5/1025 |
| work_keys_str_mv | AT yonggaidai optimizingberthallocationinmaritimetransportationwithquaycranesetuptimesusingreinforcementlearning AT zongchenli optimizingberthallocationinmaritimetransportationwithquaycranesetuptimesusingreinforcementlearning AT boyuwang optimizingberthallocationinmaritimetransportationwithquaycranesetuptimesusingreinforcementlearning |