Interterminal Truck Routing Optimization Using Deep Reinforcement Learning
| Autor: | Hyerim Bae, Yelita Anggiane Iskandar, Taufik Nur Adi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Truck
050210 logistics & transportation 021103 operations research deep reinforcement learning Operations research Computer science 05 social sciences 0211 other engineering and technologies Process (computing) Context (language use) 02 engineering and technology lcsh:Chemical technology Biochemistry Article Atomic and Molecular Physics and Optics Analytical Chemistry interterminal truck routing 0502 economics and business Container (abstract data type) Reinforcement learning lcsh:TP1-1185 Electrical and Electronic Engineering Routing (electronic design automation) Instrumentation Productivity |
| Zdroj: | Sensors, Vol 20, Iss 5794, p 5794 (2020) Sensors Volume 20 Issue 20 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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