Deep reinforcement learning based optimization of automated guided vehicle time and energy consumption in a container terminal

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10251770" target="_blank" >RIV/61989100:27240/23:10251770 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/23:10251770

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1110016822008419" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1110016822008419</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.aej.2022.12.057" target="_blank" >10.1016/j.aej.2022.12.057</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep reinforcement learning based optimization of automated guided vehicle time and energy consumption in a container terminal

Popis výsledku v původním jazyce

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 pre-sents 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 consump-tion 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 infor-mation sharing with other vehicles in the terminal.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Název v anglickém jazyce

Deep reinforcement learning based optimization of automated guided vehicle time and energy consumption in a container terminal

Popis výsledku anglicky

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 pre-sents 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 consump-tion 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 infor-mation sharing with other vehicles in the terminal.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Alexandria Engineering Journal

ISSN

1110-0168

e-ISSN

2090-2670

Svazek periodika

67

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

397-407

Kód UT WoS článku

000918221700001

EID výsledku v databázi Scopus

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