Maritime Logistics: Shipping and Port Management

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 1201

Special Issue Editors


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Guest Editor
College of Management, Shenzhen University, Shenzhen, China
Interests: green port and shipping; maritime pollution governance; sustainable port and shipping development
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Special Issue Information

Dear Colleagues,

With the continuous growth of global trade and the rapid development of digital technologies, the maritime logistics sector is facing unprecedented opportunities and challenges. The operational efficiency, management level, and collaborative capabilities of shipping and ports directly impact the effectiveness and sustainability of the entire logistics system.

This Special Issue will focus on cutting-edge research in the field of shipping and port management, including intelligent ship operations, port automation systems, multimodal transport coordination and optimization, green port management, ship emission control, maritime energy efficiency optimization, maritime supply chain resilience enhancement, and shipping. The goal of this Special Issue is to gather cutting-edge research findings, promoting knowledge exchange and technological innovation in the field of shipping and port management. We invite original research papers and review articles that collectively explore the future development directions of shipping and port management.

Prof. Dr. Jihong Chen
Prof. Dr. Xianhua Wu
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent ship operations
  • port automation
  • multimodal transport optimization
  • digital ports
  • green port management
  • ship energy efficiency and emission control
  • maritime supply chain resilience
  • intelligent scheduling and collaborative decision-making
  • maritime and port engineering

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Published Papers (1 paper)

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Research

26 pages, 4801 KB  
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
J. Mar. Sci. Eng. 2025, 13(12), 2344; https://doi.org/10.3390/jmse13122344 - 9 Dec 2025
Cited by 1 | Viewed by 764
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Maritime Logistics: Shipping and Port Management)
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