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Intelligent port logistics: A spatiotemporal knowledge graph and AI-agent framework for berth allocation

文献类型：期刊论文

作者Wang, Peng1,2; Hu, Qinyou2; Mei, Qiang3; Wang, Shaohua4,5; Yang, Yang6; Guo, Da7; Liu, Xiaotong8; Hu, Wenlong9; Chen, Jihong10
刊名ADVANCED ENGINEERING INFORMATICS
出版日期2025-11-01
卷号68页码:28
关键词Spatiotemporal knowledge graph Intelligent berth allocation Multi-agent LLMs Port logistics Yangtze River Delta
ISSN号1474-0346
DOI10.1016/j.aei.2025.103633
英文摘要Efficient berth allocation is crucial for optimizing port logistics, directly impacting ship loading, refueling, and sheltering operations while enhancing the overall efficiency of maritime logistics systems. However, the inherent uncertainty of ship arrival times often leads to inefficiencies, such as "ships waiting for berths" or "berths waiting for ships," negatively affecting port operations and economic performance. Existing berth allocation models struggle to fully capture the dynamic and complex interactions between ships and berths, particularly under spatiotemporal constraints and fluctuating maritime conditions. To address these challenges, this study proposes an intelligent berth recommendation framework, STK-LLM, which integrates Spatiotemporal Knowledge Graphs and AI-Agents. This framework constructs structured spatiotemporal triplet models to represent ship-berth interactions and employs AI-driven decision-making to enhance berth allocation strategies. An empirical analysis covering 10 ports, 49 port areas, and 2,180 berths in the Yangtze River Delta reveals competitive and cooperative berth allocation dynamics. The results indicate that the STK-LLM framework reduces ship waiting times at anchorages by approximately 20% and increases berth utilization by about 15%. This study advances intelligent port logistics by providing data-driven decision support, improving berth resource efficiency. By addressing the challenges of modeling and optimizing complex, dynamic, and uncertain ship-berth interactions-difficulties that have hindered previous berth allocation models-this framework enhances the performance of maritime logistics systems.
资助项目National Key R&D Program of China[2023YFF0805904] ; Talent introduction Program Youth Project of the Chinese Academy of Sciences[E43302020D] ; Talent introduction Program Youth Project of the Chinese Academy of Sciences[E2Z105010F] ; National Natural Science Foundation of China[52472325]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001546265700001
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/42006]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Shaohua; Chen, Jihong
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.Shanghai Maritime Univ, Merchant Marine Acad, Shanghai 200210, Peoples R China
3.Jimei Univ, Nav Coll, Xiamen 361021, Peoples R China
4.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
5.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
6.East China Normal Univ, Sch Geog Sci, Shanghai, Peoples R China
7.Beijing Univ Posts & Telecommun, Beijing Key Lab Work Safety Intelligent Monitoring, Beijing 100876, Peoples R China
8.Beijing Univ Technol, Beijing 100124, Peoples R China
9.Univ Auckland, Sch Comp Sci, Auckland, New Zealand
10.Shenzhen Univ, Coll Management, Shenzhen 518073, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Peng,Hu, Qinyou,Mei, Qiang,et al. Intelligent port logistics: A spatiotemporal knowledge graph and AI-agent framework for berth allocation[J]. ADVANCED ENGINEERING INFORMATICS,2025,68:28.
APA Wang, Peng.,Hu, Qinyou.,Mei, Qiang.,Wang, Shaohua.,Yang, Yang.,...&Chen, Jihong.(2025).Intelligent port logistics: A spatiotemporal knowledge graph and AI-agent framework for berth allocation.ADVANCED ENGINEERING INFORMATICS,68,28.
MLA Wang, Peng,et al."Intelligent port logistics: A spatiotemporal knowledge graph and AI-agent framework for berth allocation".ADVANCED ENGINEERING INFORMATICS 68(2025):28.

入库方式： OAI收割

来源：计算技术研究所

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