Use this url to cite publication: https://hdl.handle.net/20.500.14172/24617
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Deep reinforcement learning based optimization of automated guided vehicle time and energy consumption in a container terminal
Type of publication
Straipsnis Web of Science ir Scopus duomenų bazėje / Article in Web of Science and Scopus database (S1)
Type of document
type::text::journal::journal article::research article
Author(s)
VSB - Technical University of Ostrava |
Title
Deep reinforcement learning based optimization of automated guided vehicle time and energy consumption in a container terminal
Publisher
Amsterdam : Elsevier
Date Issued
| Date Issued | Volume | Start Page | End Page |
|---|---|---|---|
2023-01-03 | vol. 67 | 397 | 407 |
Is part of
Alexandria engineering journal
Field of Science
Abstract
The energy efficiency of port container terminal equipment and the reduction of CO2 emissions are among one of the biggest challenges facing every seaport in the world. The article presents the modeling of the container transportation process in a terminal from the quay crane to the stack using battery-powered Automated Guided Vehicle (AGV) to estimate the energy consumption parameters. An AGV speed control algorithm based on Deep Reinforcement Learning (DRL) is proposed to optimize the energy consumption of container transportation. The results obtained and compared with real transportation measurements showed that the proposed DRL-based approach dynamically changing the driving speed of the AGV reduces energy consumption by 4.6%. The obtained results of the research provide the prerequisites for further research in order to find optimal strategies for autonomous vehicle movement including context awareness and information sharing with other vehicles in the terminal.
ISSN (of the container)
1110-0168
2090-2670
WOS
000918221700001
Scopus
2-s2.0-85145979096
Other Identifier(s)
1-s2.0-S1110016822008419
Coverage Spatial
Nyderlandai / Netherlands (NL)
Language
Anglų / English (en)
Bibliographic Details
36
Project(s)
Research Projects Implemented by World-class Researcher Groups
Access Rights
Atviroji prieiga / Open Access
| Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
|---|---|---|---|---|---|---|---|---|
Alexandria Engineering Journal | 6.8 | 3.8 | 3.8 | 3.8 | 1 | 1.789 | 2022 | Q1 |
| Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
|---|---|---|---|---|---|---|---|---|
Alexandria Engineering Journal | 6.8 | 3.8 | 3.8 | 3.8 | 1 | 1.789 | 2022 | Q1 |
3.8 | ||||||||
3.684 |
| Journal | Cite Score | SNIP | SJR | Year | Quartile |
|---|---|---|---|---|---|
AEJ - Alexandria Engineering Journal | 9.1 | 2.006 | 0.933 | 2022 | Q1 |