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Maritime Logistics: Shipping and Port Management
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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: 10 September 2026 | Viewed by 4621

Editors

Prof. Dr. Jihong Chen

E-Mail Website
Guest Editor
College of Management, Shenzhen University, Shenzhen, China
Interests: green port and shipping; maritime pollution governance; sustainable port and shipping development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xianhua Wu

E-Mail Website
Guest Editor
School of Economics & Management, Shanghai Maritime University, Shanghai, China
Interests: shipping low-carbon management; shipping big data management; maritime risk analysis; emergency management
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

27 pages, 1294 KB  
Article
A Data-Driven Framework for Predicting Truck Turnaround Times in Maritime Terminals: Flow-Aware Models
by Enzzo Ayala-Peña, Raimundo Vogel, Javier González-Salazar, Sebastián Muñoz-Herrera, Rosa G. González-Ramírez and Karol Suchan
J. Mar. Sci. Eng. 2026, 14(15), 1392; https://doi.org/10.3390/jmse14151392 - 29 Jul 2026
Viewed by 359
Abstract
Truck Turnaround Time (TTT), defined as the total gate-to-gate time during a truck visit to a container terminal, is a critical performance indicator of landside operations. Although machine learning has been applied to TTT prediction, existing studies typically pool import and export movements [...] Read more.
Truck Turnaround Time (TTT), defined as the total gate-to-gate time during a truck visit to a container terminal, is a critical performance indicator of landside operations. Although machine learning has been applied to TTT prediction, existing studies typically pool import and export movements into a single model, overlooking operational heterogeneity between flows. Moreover, little attention is paid to analyzing the importance of factors that enable TTT prediction. This study develops a flow-disaggregated predictive framework for a Chilean container terminal using 754,568 export and 1,056,351 import truck visits recorded between 2017 and 2023. Four tree-based ensembles and three neural network architectures are benchmarked under a chronological train/test split. Tree-based models tend to achieve marginally lower errors, though differences are small and not uniform across flows. A pronounced asymmetry emerges: import predictions are substantially more accurate (MAE = 6.79 min; WAPE = 34.36%) than export predictions (MAE = 32.49 min; WAPE = 52.53%), and this gap persists after normalizing for differences in mean TTT. TreeSHAP analysis identifies distinct predictive structures: export TTT is primarily associated with gate congestion and maritime service activity, while import TTT is more strongly associated with intra-terminal travel distance and crane operator experience. The higher Gini concentration and bidirectionality of dominant export predictors are consistent with unobserved drivers—such as the states of inspection queues (customs, sanitary, etc.) and the off-dock truck staging area—that limit predictive accuracy beyond process variability alone. In the integrated model, flow-identifying variables rank among the most influential features, providing empirical support for flow disaggregation. These findings indicate that flow-specific modeling improves both accuracy and interpretability in operationally heterogeneous terminal processes. Full article
(This article belongs to the Special Issue Maritime Logistics: Shipping and Port Management)
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29 pages, 2596 KB  
Article
Integrated Rescheduling for Vessels and Tugboats in One-Way Channel Ports Under Adverse Weather Conditions
by Shiyan Jia and Hongxing Zheng
J. Mar. Sci. Eng. 2026, 14(5), 512; https://doi.org/10.3390/jmse14050512 - 9 Mar 2026
Viewed by 585
Abstract
To address the significant operational disruptions caused by inclement weather in maritime logistics, this study investigates the integrated rescheduling optimization of vessels and tugboats within one-way channel ports. The research aims to minimize total operational costs, including dispatching and delay penalties, by synchronizing [...] Read more.
To address the significant operational disruptions caused by inclement weather in maritime logistics, this study investigates the integrated rescheduling optimization of vessels and tugboats within one-way channel ports. The research aims to minimize total operational costs, including dispatching and delay penalties, by synchronizing vessel movements with tugboat service capabilities under uncertain conditions. Methodologically, a rolling horizon decision-making mechanism is proposed to accommodate dynamic operational scenarios driven by fluctuating weather. On this basis, an integrated rescheduling model is developed to address the compounded challenges of navigation rule changes, channel closures, vessel delays, and additional shifting tasks. The model explicitly incorporates critical constraints such as channel navigation protocols, tugboat availability, power capacity limits, and tidal windows for deep-draft vessels. To achieve efficient solution generation, an improved Variable Neighborhood Search (VNS) algorithm is designed to effectively handle the problem’s complexity. Experimental results validate the effectiveness of the proposed approach and the robustness of the algorithm in diverse disruption scenarios. Furthermore, sensitivity analyses reveal how channel closure duration, vessel delay intensities, and the volume of shifting tasks quantitatively influence rescheduling outcomes. This study contributes a novel synergistic optimization framework that enhances the operational resilience and decision-making capabilities of port authorities. Full article
(This article belongs to the Special Issue Maritime Logistics: Shipping and Port Management)
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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 3 | Viewed by 2429
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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