Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171767
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dc.contributor.authorGao, Yinpingen_US
dc.contributor.authorChen, Chun-Hsienen_US
dc.contributor.authorChang, Daofangen_US
dc.date.accessioned2023-11-07T07:32:59Z-
dc.date.available2023-11-07T07:32:59Z-
dc.date.issued2023-
dc.identifier.citationGao, Y., Chen, C. & Chang, D. (2023). A machine learning-based approach for multi-AGV dispatching at automated container terminals. Journal of Marine Science and Engineering, 11(7), 1407-. https://dx.doi.org/10.3390/jmse11071407en_US
dc.identifier.issn2077-1312en_US
dc.identifier.urihttps://hdl.handle.net/10356/171767-
dc.description.abstractThe dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Marine Science and Engineeringen_US
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleA machine learning-based approach for multi-AGV dispatching at automated container terminalsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doi10.3390/jmse11071407-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85166257702-
dc.identifier.issue7en_US
dc.identifier.volume11en_US
dc.identifier.spage1407en_US
dc.subject.keywordsAGV Dispatchingen_US
dc.subject.keywordsDistribution Balanceen_US
item.grantfulltextopen-
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