Collaborative optimization of truck scheduling in container terminals using graph theory and DDQN

Abstract The container terminal is a key node in global trade and logistics, where trucks connect quay cranes, storage yards, and vessels. Optimizing truck scheduling is crucial for enhancing port efficiency by addressing issues such as low truck utilization, excessive quay crane waiting times, and...

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Bibliographic Details
Published in:	Scientific Reports
Main Authors:	Shu Cheng, Qianyu Liu, Heng Jin, Ran Zhang, Longhua Ma, Chiew Foong Kwong
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-02-01
Subjects:	Container terminal Truck scheduling Reinforcement learning DDQN
Online Access:	https://doi.org/10.1038/s41598-025-91140-7

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