A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals
The dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between sto...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/7/1407 |
| _version_ | 1827732762463830016 |
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| author | Yinping Gao Chun-Hsien Chen Daofang Chang |
| author_facet | Yinping Gao Chun-Hsien Chen Daofang Chang |
| author_sort | Yinping Gao |
| collection | DOAJ |
| description | The dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP. |
| first_indexed | 2024-03-11T00:56:38Z |
| format | Article |
| id | doaj.art-589ef3a0eeaa43ce9178fb6da6cd2ae4 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T00:56:38Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-589ef3a0eeaa43ce9178fb6da6cd2ae42023-11-18T19:59:49ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-07-01117140710.3390/jmse11071407A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container TerminalsYinping Gao0Chun-Hsien Chen1Daofang Chang2School of Management, Shanghai University, 99 Shangda Road, Shanghai 200444, ChinaSchool of Mechanical & Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, SingaporeLogistics Engineering College, Shanghai Maritime University, 1550 Haigang Avenue, Shanghai 201306, ChinaThe dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP.https://www.mdpi.com/2077-1312/11/7/1407AGV dispatchingdistribution balanceenergy consumptionQ-learningmultiagent |
| spellingShingle | Yinping Gao Chun-Hsien Chen Daofang Chang A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals Journal of Marine Science and Engineering AGV dispatching distribution balance energy consumption Q-learning multiagent |
| title | A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals |
| title_full | A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals |
| title_fullStr | A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals |
| title_full_unstemmed | A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals |
| title_short | A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals |
| title_sort | machine learning based approach for multi agv dispatching at automated container terminals |
| topic | AGV dispatching distribution balance energy consumption Q-learning multiagent |
| url | https://www.mdpi.com/2077-1312/11/7/1407 |
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