A digital twin-based approach for optimizing operation energy consumption at automated container terminals

The sustainable development of port operation management is strongly related to the energy consumption of production at automated container terminals (ACTs). This paper focuses on the production activities at a container yard, which is the primary facility of ACTs. A digital twin-based approach is p...

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Main Authors: Gao, Yinping, Chang, Daofang, Chen, Chun-Hsien
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/169134
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1691342023-07-03T04:13:29Z A digital twin-based approach for optimizing operation energy consumption at automated container terminals Gao, Yinping Chang, Daofang Chen, Chun-Hsien School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Sustainable Development Energy Consumption The sustainable development of port operation management is strongly related to the energy consumption of production at automated container terminals (ACTs). This paper focuses on the production activities at a container yard, which is the primary facility of ACTs. A digital twin-based approach is proposed to optimize the operation of an automatic stacking crane (ASC) handling containers in terms of energy consumption. A virtual container yard that syncs with a physical container yard in the ACT digital twin system for observation and validation is developed. A mathematical model is established to minimize the total energy consumption of completing all tasks. Then, the Q-learning algorithm is adapted to optimize a solution based on the operating data from the ACT digital twin system. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach by comparing it with two other solution algorithms, viz., genetic algorithm (GA) and particle swarm optimization (PSO). The total energy consumption of two operation strategies (i.e., centralized and decentralized) are also compared using the proposed digital twin-based approach. With digital twin, the operational environment and energy consumption are visualized to support optimization and management of ASCs. Managers and operators can choose an appropriate strategy according to the designated sustainable goal. This work was supported by the National Key Research and Development Plan of China (No. 2019YFB1704403). 2023-07-03T04:13:29Z 2023-07-03T04:13:29Z 2023 Journal Article Gao, Y., Chang, D. & Chen, C. (2023). A digital twin-based approach for optimizing operation energy consumption at automated container terminals. Journal of Cleaner Production, 385, 135782-. https://dx.doi.org/10.1016/j.jclepro.2022.135782 0959-6526 https://hdl.handle.net/10356/169134 10.1016/j.jclepro.2022.135782 2-s2.0-85145022012 385 135782 en Journal of Cleaner Production © 2022 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
Sustainable Development
Energy Consumption
spellingShingle Engineering::Mechanical engineering
Sustainable Development
Energy Consumption
Gao, Yinping
Chang, Daofang
Chen, Chun-Hsien
A digital twin-based approach for optimizing operation energy consumption at automated container terminals
description The sustainable development of port operation management is strongly related to the energy consumption of production at automated container terminals (ACTs). This paper focuses on the production activities at a container yard, which is the primary facility of ACTs. A digital twin-based approach is proposed to optimize the operation of an automatic stacking crane (ASC) handling containers in terms of energy consumption. A virtual container yard that syncs with a physical container yard in the ACT digital twin system for observation and validation is developed. A mathematical model is established to minimize the total energy consumption of completing all tasks. Then, the Q-learning algorithm is adapted to optimize a solution based on the operating data from the ACT digital twin system. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach by comparing it with two other solution algorithms, viz., genetic algorithm (GA) and particle swarm optimization (PSO). The total energy consumption of two operation strategies (i.e., centralized and decentralized) are also compared using the proposed digital twin-based approach. With digital twin, the operational environment and energy consumption are visualized to support optimization and management of ASCs. Managers and operators can choose an appropriate strategy according to the designated sustainable goal.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Gao, Yinping
Chang, Daofang
Chen, Chun-Hsien
format Article
author Gao, Yinping
Chang, Daofang
Chen, Chun-Hsien
author_sort Gao, Yinping
title A digital twin-based approach for optimizing operation energy consumption at automated container terminals
title_short A digital twin-based approach for optimizing operation energy consumption at automated container terminals
title_full A digital twin-based approach for optimizing operation energy consumption at automated container terminals
title_fullStr A digital twin-based approach for optimizing operation energy consumption at automated container terminals
title_full_unstemmed A digital twin-based approach for optimizing operation energy consumption at automated container terminals
title_sort digital twin-based approach for optimizing operation energy consumption at automated container terminals
publishDate 2023
url https://hdl.handle.net/10356/169134
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