Next Article in Journal
Optimization of Vortex Well Alga Extractor Based on PSO-GP
Next Article in Special Issue
Integrated Rescheduling for Vessels and Tugboats in One-Way Channel Ports Under Adverse Weather Conditions
Previous Article in Journal
Spatial Analysis of Extreme Coastal Water Levels and Dominant Forcing Factors Along the Senegalese Coast
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JMSE
Volume 13
Issue 12
10.3390/jmse13122344
jmse-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Simulation and Optimization of Collaborative Scheduling of AGV and Yard Crane in U-Shaped Automated Terminal Based on Deep Reinforcement Learning

by
Yongsheng Yang
*,
Feiteng Zhao
,
Junkai Feng
,
Shu Sun
,
Wenying Lu
and
Shanghao Chen
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(12), 2344; https://doi.org/10.3390/jmse13122344
Submission received: 4 November 2025 / Revised: 22 November 2025 / Accepted: 7 December 2025 / Published: 9 December 2025
(This article belongs to the Special Issue Maritime Logistics: Shipping and Port Management)
Browse Figures
Versions Notes

Abstract

In U-shaped automated container terminals (U-shaped ACTs), automated guided vehicles (AGVs) need to frequently interact with yard cranes (YCs), and separate scheduling of the two devices will affect terminal efficiency. Therefore, this study explores the coordinated scheduling problem between the two devices. To solve this problem, a high-precision simulation model of the U-shaped ACTs is established, which incorporates real operational logic. Second, an Improved Non-dominated Sorting Genetic Algorithm II based on Proximal Policy Optimization (INSGAII-PPO) is proposed. The algorithm uses PPO to realize dynamic genetic operator selection and makes related improvements, which improve the multi-objective optimization ability of NSGAII, and solve the collaborative scheduling problem by combining simulation. Finally, a hybrid weighted Technique for Order Preference by Similarity to Ideal Solution with preferences is proposed to select the final solution. The experimental results show that the scheme obtained by INSGAII-PPO exhibits better convergence and diversity, and offers significant advantages compared with the comparison algorithms. Moreover, the energy consumption and waiting time of the final solution selected by the proposed method are reduced by 3.42% and 4.87% on average. The proposed method has the capability of providing a theoretical reference for the AGVs and YCs collaborative scheduling of U-shaped ACTs.
Keywords: U-shaped automated container terminal; collaborative scheduling; multi-objective optimization; simulation optimization; deep reinforcement learning U-shaped automated container terminal; collaborative scheduling; multi-objective optimization; simulation optimization; deep reinforcement learning

Share and Cite

MDPI and ACS Style

Yang, Y.; Zhao, F.; Feng, J.; Sun, S.; Lu, W.; Chen, S. Simulation and Optimization of Collaborative Scheduling of AGV and Yard Crane in U-Shaped Automated Terminal Based on Deep Reinforcement Learning. J. Mar. Sci. Eng. 2025, 13, 2344. https://doi.org/10.3390/jmse13122344

AMA Style

Yang Y, Zhao F, Feng J, Sun S, Lu W, Chen S. Simulation and Optimization of Collaborative Scheduling of AGV and Yard Crane in U-Shaped Automated Terminal Based on Deep Reinforcement Learning. Journal of Marine Science and Engineering. 2025; 13(12):2344. https://doi.org/10.3390/jmse13122344

Chicago/Turabian Style

Yang, Yongsheng, Feiteng Zhao, Junkai Feng, Shu Sun, Wenying Lu, and Shanghao Chen. 2025. "Simulation and Optimization of Collaborative Scheduling of AGV and Yard Crane in U-Shaped Automated Terminal Based on Deep Reinforcement Learning" Journal of Marine Science and Engineering 13, no. 12: 2344. https://doi.org/10.3390/jmse13122344

APA Style

Yang, Y., Zhao, F., Feng, J., Sun, S., Lu, W., & Chen, S. (2025). Simulation and Optimization of Collaborative Scheduling of AGV and Yard Crane in U-Shaped Automated Terminal Based on Deep Reinforcement Learning. Journal of Marine Science and Engineering, 13(12), 2344. https://doi.org/10.3390/jmse13122344

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop