Interterminal Truck Routing Optimization Using Deep Reinforcement Learning
The continued growth of the volume of global containerized transport necessitates that most of the major ports in the world improve port productivity by investing in more interconnected terminals. The development of the multiterminal system escalates the complexity of the container transport process...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/20/5794 |
| _version_ | 1797551049565798400 |
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| author | Taufik Nur Adi Yelita Anggiane Iskandar Hyerim Bae |
| author_facet | Taufik Nur Adi Yelita Anggiane Iskandar Hyerim Bae |
| author_sort | Taufik Nur Adi |
| collection | DOAJ |
| description | The continued growth of the volume of global containerized transport necessitates that most of the major ports in the world improve port productivity by investing in more interconnected terminals. The development of the multiterminal system escalates the complexity of the container transport process and increases the demand for container exchange between different terminals within a port, known as interterminal transport (ITT). Trucks are still the primary modes of freight transportation to transport containers among most terminals. A trucking company needs to consider proper truck routing planning because, based on several studies, it played an essential role in coordinating ITT flows. Furthermore, optimal truck routing in the context of ITT significantly affects port productivity and efficiency. The study of deep reinforcement learning in truck routing optimization is still limited. In this study, we propose deep reinforcement learning to provide truck routes of a given container transport order by considering several significant factors such as order origin, destination, time window, and due date. To assess its performance, we compared between the proposed method and two approaches that are used to solve truck routing problems. The experiment results showed that the proposed method obtains considerably better results compared to the other algorithms. |
| first_indexed | 2024-03-10T15:39:14Z |
| format | Article |
| id | doaj.art-259850ea36dd4352a49137c58a16f7b3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:39:14Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-259850ea36dd4352a49137c58a16f7b32023-11-20T16:56:50ZengMDPI AGSensors1424-82202020-10-012020579410.3390/s20205794Interterminal Truck Routing Optimization Using Deep Reinforcement LearningTaufik Nur Adi0Yelita Anggiane Iskandar1Hyerim Bae2Department of Industrial Engineering, Pusan National University, Busan 46241, KoreaDepartment of Industrial Engineering, Pusan National University, Busan 46241, KoreaDepartment of Industrial Engineering, Pusan National University, Busan 46241, KoreaThe continued growth of the volume of global containerized transport necessitates that most of the major ports in the world improve port productivity by investing in more interconnected terminals. The development of the multiterminal system escalates the complexity of the container transport process and increases the demand for container exchange between different terminals within a port, known as interterminal transport (ITT). Trucks are still the primary modes of freight transportation to transport containers among most terminals. A trucking company needs to consider proper truck routing planning because, based on several studies, it played an essential role in coordinating ITT flows. Furthermore, optimal truck routing in the context of ITT significantly affects port productivity and efficiency. The study of deep reinforcement learning in truck routing optimization is still limited. In this study, we propose deep reinforcement learning to provide truck routes of a given container transport order by considering several significant factors such as order origin, destination, time window, and due date. To assess its performance, we compared between the proposed method and two approaches that are used to solve truck routing problems. The experiment results showed that the proposed method obtains considerably better results compared to the other algorithms.https://www.mdpi.com/1424-8220/20/20/5794interterminal truck routingdeep reinforcement learning |
| spellingShingle | Taufik Nur Adi Yelita Anggiane Iskandar Hyerim Bae Interterminal Truck Routing Optimization Using Deep Reinforcement Learning Sensors interterminal truck routing deep reinforcement learning |
| title | Interterminal Truck Routing Optimization Using Deep Reinforcement Learning |
| title_full | Interterminal Truck Routing Optimization Using Deep Reinforcement Learning |
| title_fullStr | Interterminal Truck Routing Optimization Using Deep Reinforcement Learning |
| title_full_unstemmed | Interterminal Truck Routing Optimization Using Deep Reinforcement Learning |
| title_short | Interterminal Truck Routing Optimization Using Deep Reinforcement Learning |
| title_sort | interterminal truck routing optimization using deep reinforcement learning |
| topic | interterminal truck routing deep reinforcement learning |
| url | https://www.mdpi.com/1424-8220/20/20/5794 |
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