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Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering

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A Special Issue of Journal of Marine Science and Engineering (ISSN 2077-1312) belonging to the section "Ocean Engineering".

Deadline for manuscript submissions: 25 December 2026 | Viewed by 4616

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Editors

Prof. Dr. Guangnian Xiao

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Guest Editor

School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
Interests: intelligent transportation; green shipping; sustainable transportation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chao Mi

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Guest Editor

College of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: port equipment automation; intelligent traffic equipment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, global maritime trade is operating within a complex and dynamic macro-environment. Over 80% of global trade relies on maritime transport, and trade volumes continue to rise. UNCTAD notes that global maritime trade reached 12.3 billion tons in 2023, grew by 2.4% to 12.6 billion tons in 2024, and is projected to reach approximately 12.9 billion tons in 2025. In this context, sustainable shipping, port intelligence, and marine environmental engineering have become critical drivers for the green transformation of the ocean economy. Simultaneously, with the interconnectivity of global supply chains, the intelligent and green transformation of ports and land transport is increasingly vital to address climate change, supply chain disruptions, and efficiency challenges.

Based on this, JMSE is inviting Prof. Chao Mi and Prof. Guangnian Xiao to establish a Special Issue entitled "Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering". This Special Issue aims to explore how technological innovation and engineering solutions can enhance maritime operational efficiency, minimize environmental impact, and promote smarter, more sustainable port management and international supply chain integration. We invite scholars and industry experts worldwide to share their research findings and contribute valuable insights to the sustainable development of the global shipping, port, and supply chain industries.

Historically, maritime transport has evolved from traditional labor-intensive operations to the era of containerization and, more recently, into the age of digitalization and automation (Industry 4.0). While early developments focused primarily on scale and speed to meet growing trade demands, the focus has shifted significantly in the last decade. The industry is now transitioning from purely economic-driven logistics to a complex ecosystem that prioritizes environmental sustainability and ecological safety. This evolution has given rise to the integration of Marine Environmental Engineering with port operations, marking a new historical stage where smart technologies are not just used for efficiency, but also for pollution control, decarbonization, and ecosystem protection. This Special Issue builds upon this historical trajectory, addressing the urgent need to merge legacy infrastructure with modern intelligence and green engineering standards.

Current cutting-edge research lies at the intersection of digitalization, decarbonization, and environmental resilience. We are witnessing a surge in studies focusing on Digital Twins for port management, which allow for real-time monitoring of both logistical flows and environmental parameters (such as water quality and emissions). In the realm of Marine Environmental Engineering, advanced research includes the development of eco-friendly dredging techniques, noise reduction technologies for ships, and ballast water treatment systems driven by AI optimization. Furthermore, the application of Blockchain and Big Data for transparent, low-carbon supply chain management represents a significant frontier. Scholars are also exploring the engineering challenges of retrofitting ports for alternative fuels (e.g., hydrogen, ammonia) and designing climate-resilient maritime infrastructure to withstand extreme weather events.

What kind of paper we are looking for:

This Special Issue will cover review articles and original research on "Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering". We will focus on key issues such as intelligent operations management, smart equipment in maritime and port operations, sustainable shipping strategies, innovative practices in port management, marine environmental engineering applications, and the optimization and green transformation of international supply chains. Our goal is to provide useful ideas and solutions for the industry, including, but not limited to, the following topics:

Climate Change and Carbon Reduction Pathways in Shipping: Research on the impact of climate change on shipping and how the industry can achieve carbon reduction goals through technical innovation and policy guidance.
Port Digital Transformation and Smart Development: Analysis of digital and intelligent technologies in port management to improve operational efficiency and service quality.
Green Port Development and Marine Environmental Engineering: Case studies on green and low-carbon transitions in ports, discussing engineering measures to reduce environmental impact, manage waste, and promote green port construction.
Collaborative Innovation in Shipping and Ports: Analysis of collaboration between shipping and port sectors in technology innovation and service optimization.
Intelligent Operations Management in Maritime and Ports: Exploring the integration of digital technologies and AI to enhance operational efficiency and decision-making capabilities.
Smart Equipment in Maritime and Port Operations: Investigating advancements in smart maritime and port equipment, including automated vessels and intelligent cargo handling systems.
International Maritime Supply Chain Management: Exploring optimization strategies for global maritime supply chains, including resilience, risk management, and multimodal integration.
Port Intelligence and Green Transformation: Analyzing the application of AI, IoT, and big data in smart ports, as well as green operations such as renewable energy use.
Intelligence and Greening of Port and Land Transport: Research on the seamless connection between ports and land transport (rail, road), including smart traffic systems and electrification strategies.
Marine Environmental Engineering Technologies: Engineering solutions for pollution control, marine ecological protection, and sustainable infrastructure in maritime settings.

Prof. Dr. Guangnian Xiao
Prof. Dr. Chao Mi
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

sustainable maritime transport
port intelligence
marine environmental engineering
supply chain management
green shipping
carbon reduction
digital transformation
smart logistics
multimodal transport

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Published Papers (8 papers)

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Research

30 pages, 5641 KB

Open AccessArticle

A Method for Portal Crane Wire Rope Recognition Based on Improved PointNet++

by Xinyuan Li, Yujie Zhang and Yang Shen

J. Mar. Sci. Eng. 2026, 14(16), 1463; https://doi.org/10.3390/jmse14161463 - 8 Aug 2026

Viewed by 194

Abstract

In automated dry bulk terminal operations, accurate perception of the spatial pose of portal crane wire ropes is important for grab positioning and can provide geometric information for subsequent anti-sway control research. Vision-based measurements may be affected by metallic reflections, illumination variation, and dust occlusion, whereas inertial or mechanically coupled measurements may be affected by vibration and dynamic coupling. This study proposes a LiDAR-based method for wire rope point cloud segmentation and pose estimation using an improved PointNet++. Dual-LiDAR point clouds are aligned and filtered using a kinematic constraint-based Region of Interest (ROI) to reduce background redundancy. A Spatial Self-Attention (SSA) module is introduced to combine long-range semantic dependencies with local spatial weighting, improving the representation of sparse and fragmented wire rope points. The segmented wire rope points are separated by t–k-means clustering and fitted with spatial lines for pose estimation. The complete acquisition comprises 11,348 annotated frames: a 9458-frame model development dataset from 1000 complete operating cycles, and a separately retained 1890-frame independent engineering test set from 200 condition-specific operating sequences. The development dataset was divided into mutually exclusive training and validation partitions at the level of complete operating cycles, and checkpoint selection was performed only on the validation set. Three independent training runs with fixed random seeds were conducted. On the independent test set, PointNet++ achieved an F1-score of 87.5 ± 0.2% and an mIoU of 79.0 ± 0.2%, whereas the complete proposed method achieved an F1-score of 92.8 ± 0.2% and an mIoU of 86.6 ± 0.2%. These results characterize performance on independent operating sequences collected from the crane and sensor configurations represented in the dataset. The standalone segmentation stage achieved 111.9 FPS, whereas the complete processing pipeline required slightly more than 2 s per frame because of frame-by-frame KD-ICP fine registration. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

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22 pages, 3656 KB

Open AccessArticle

Decoupling Causality from Correlation in Port Operations: A Small-Sample DML Approach for Sea–Rail Intermodal Systems

by Panfeng Hao, Li Wang, Xiaoning Zhu and Jiayu Liu

J. Mar. Sci. Eng. 2026, 14(14), 1338; https://doi.org/10.3390/jmse14141338 - 21 Jul 2026

Viewed by 349

Abstract

Container sea–rail intermodal transport is pivotal to the low-carbon transformation of global supply chains. However, traditional performance evaluation systems are prone to circular reasoning fallacies due to the nesting of input and output indicators and frequently suffer from spurious regression when analyzing high-dimensional macro time series under small-sample constraints. To address these endogeneity and attribution challenges, this study proposes a four-step progressive causal inference framework. Taking Tianjin Port—a pioneering hub of China’s “road-to-rail” freight restructuring policy—as the empirical subject, we use quarterly operational data covering a complete cycle from 2017Q1 to 2024Q4. First, we construct a strictly exogenous high-quality development index based on turnover efficiency, logistics cost reduction, and carbon emission mitigation, which completely isolates scale input factors. Second, from an initial pool of 35 operational and macroeconomic indicators, 17 candidate variables are rigorously pre-screened according to statistical consistency and logistics system theory. Third, an adaptive Double Machine Learning (DML) model integrated with leave-one-out cross-fitting is applied to disentangle complex collinearity among variables. The results show that DML effectively eliminates confounding noise, accurately identifies 15 true causal drivers, and excludes spurious correlations such as redundant macro-infrastructure investment. Furthermore, a causally weighted composite index reveals that the intermodal system exhibits strong resilience to global supply chain fluctuations and has undergone a four-stage evolution. Its development momentum has fundamentally shifted from extensive scale expansion to a refined mode driven by the synergy of efficiency and service quality. This study provides a robust methodological paradigm for port performance evaluation and targeted decision support for resource allocation. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

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32 pages, 28681 KB

Open AccessArticle

PLC-Guided Vibration Measurement and Condition-Aware State Characterization of Quay Crane Hoisting Gearboxes Under Non-Stationary Field Operation

by Weiguo Zhang, Mingfei Ai, Xiangkun Zeng, Meizhen Li, Dongsheng Wang, Yang Shen and Ning Zhao

J. Mar. Sci. Eng. 2026, 14(14), 1314; https://doi.org/10.3390/jmse14141314 - 17 Jul 2026

Viewed by 252

Abstract

Field vibration measurements of quay-crane hoisting gearboxes are difficult to interpret because the measured response is generated under short, load-dependent cycles rather than stationary excitation. This study develops a PLC-guided method for constructing condition-tagged steady-state vibration samples from non-stationary field measurements. Multi-rate records are aligned on a common time basis; work cycles and action stages are identified from load and hoist-speed information; steady PLC candidates are selected using operating-context and local-steadiness criteria; and one-second vibration segments are fine-screened within the corresponding search intervals. The accepted segments are stored in a quality-controlled feature table and characterized by acceleration RMS, 10–1000 Hz velocity RMS, kurtosis, peak-to-peak acceleration, dominant frequency, and spectral entropy. Field data from four gearboxes yielded 1140 steady-state segments and 8912 quality-controlled segment-channel records. Stepwise CV analysis showed that action-only grouping reduced the coefficient of variation from 0.692 to 0.658, while full machine–action–channel grouping reduced it to 0.351, corresponding to a 49.35% reduction. Median acceleration RMS ranged from 0.494 to 4.805 m/s², and 10–1000 Hz velocity RMS ranged from 0.317 to 1.512 mm/s. The method provides a traceable basis for condition-aware baseline modelling and trend analysis without making unsupported fault-diagnostic claims. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

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25 pages, 2169 KB

Open AccessArticle

Batch-Level Instruction Sequencing for Container Terminals via Parallel Priority-Driven Deep Q-Networks

by Xueqiang Du, Bencheng Luo, Tianzeng Shao, Lu Dou, Jieting Zhao, Jing Wang and Yixuan Zhang

J. Mar. Sci. Eng. 2026, 14(14), 1292; https://doi.org/10.3390/jmse14141292 - 14 Jul 2026

Viewed by 402

Abstract

Sequential decision-making is a critical process that governs the loading and unloading operations of container terminals through a series of instructions. The quality of these batch-level dispatching decisions directly determines operational efficiency and the competitive position of the terminal. This paper proposes an intelligent instruction sequencing model based on a Parallel Priority-driven Deep Q-Network (ppDDQN) algorithm, acting as a centralized instruction dispatcher. The proposed ppDDQN extends standard Double DQN by incorporating (i) a parallel priority mechanism that integrates five domain-specific decision features—yard crane movement, container flipping, loading sequence, operational conflicts, and task completion potential—into the experience replay prioritization, and (ii) a wake–sleep feature learning architecture for structured state representation. A comprehensive simulation study with 100 validation cases across three scales (29, 50, and 100 containers) demonstrates that ppDDQN achieves a 20.0% improvement in normalized objective value over the genetic algorithm baseline and a 9.0% improvement over the step-activation variant (with statistical significance p < 0.01), while maintaining feasibility in over 96% of test cases across all scales. The proposed method effectively mitigates yard crane travel distance, limits container rehandling, and resolves operational conflicts, providing a robust batch-level sequencing solution under multi-equipment operational constraints. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

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24 pages, 2686 KB

Open AccessArticle

Research on Simulation Optimization of ART Charging Strategies in Automated Container Terminals

by Hongqi Huang, Rong Yang, Mengjie He, Nenad Zrnic, Ning Zhao and Xiangwei Liu

J. Mar. Sci. Eng. 2026, 14(13), 1183; https://doi.org/10.3390/jmse14131183 - 27 Jun 2026

Viewed by 327

Abstract

In automated container terminals, Autonomous Rail-guided Transporters (ARTs) are responsible for horizontal transportation tasks between quay cranes and yard blocks during unloading operations. Their charging strategies directly affect operational continuity, charging resource utilization, and road traffic load. To improve the consistency between the simulation environment and the actual terminal layout, this study constructs a DXF-based directed traffic network based on the CAD/DXF layout of Tianjin Port Second Container Terminal. A coupled discrete-event simulation model integrating ART operations, charging behavior, and traffic dynamics is developed using SimPy. The study further compares several charging strategies, including threshold charging, conventional opportunity charging, safe opportunity charging, interval charging, and Distance-Aware Interval Charging (DAIC). The results indicate that the conventional opportunity charging strategy suffers from battery depletion failures under continuous unloading task flows due to the absence of mandatory low-battery protection. After introducing a safety threshold, the safe opportunity charging strategy effectively eliminates the risk of battery depletion. Considering comprehensive performance indicators, including operational success rate, task completion time, charging travel distance, charging frequency, minimum State of Charge (SOC), and road congestion level, the 40%/70% interval charging strategy demonstrates strong overall robustness, while the 40%/80% interval charging strategy shows advantages in completion time and charging resource utilization. Both strategies can therefore be regarded as key candidate charging schemes for ART operations in Tianjin Port Second Container Terminal. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

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29 pages, 3734 KB

Open AccessArticle

Q-Learning-Based Sailing Speed Optimization for Ocean-Going Liners Under the EU ETS: Considering Shipper Satisfaction

by Tong Zhou, Tiantian Bao, Yifan Liu and Chuanqiu Zhang

J. Mar. Sci. Eng. 2026, 14(9), 848; https://doi.org/10.3390/jmse14090848 - 30 Apr 2026

Viewed by 440

Abstract

With the formal inclusion of the shipping industry in the European Union Emissions Trading System (EU ETS), the speed optimization of ocean-going container ships must simultaneously balance operating costs, incorporating carbon emission costs and shipper satisfaction with transportation timeliness. Taking ocean-going container liner routes as the research object, this paper establishes a ship navigation resistance model based on meteorological and hydrological conditions, and constructs a route segmentation mechanism and a ship fuel consumption model on this basis. The spatially differentiated carbon accounting rules of the EU ETS are introduced, a fuzzy membership function is adopted to quantify shipper satisfaction, and a Q-learning-based solution algorithm for ship speed optimization that balances operating costs and shipper satisfaction is designed. Numerical experiments on a 20,150 Twenty-foot Equivalent Unit (TEU) container ship demonstrate that the proposed framework reduces total operating costs by 5.56%, EU ETS carbon compliance costs by 18.72%, and total voyage carbon emissions by 11.01% compared with the conventional constant-speed strategy. Meanwhile, the algorithm can spontaneously form an optimal speed strategy adapted to meteorological conditions and policy rules. Through parameter sensitivity analysis, this paper further extracts management implications for liner-operating companies. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

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29 pages, 4475 KB

Open AccessArticle

Seamless Task Scheduling for Vehicle-Crane Coordination in Container Terminals: A Spatio-Temporal Optimization Approach

by Xingyu Wang, Xiangwei Liu, Jintao Lai, Weimeng Lin, Qiang Ling, Yang Shen, Ning Zhao and Jia Hu

J. Mar. Sci. Eng. 2026, 14(7), 614; https://doi.org/10.3390/jmse14070614 - 26 Mar 2026

Viewed by 903

Abstract

Task scheduling for vehicle–crane coordination is crucial for the operational efficiency of electrified automated container terminals (ACTs). However, under fully shared dispatching, existing studies rarely capture how charging-induced capacity fluctuations disrupt bidirectional service–arrival matching and propagate service-window shifts. To address this gap, this study proposes a comprehensive spatio-temporal optimization approach. Firstly, a bi-objective model is established to minimize service–arrival mismatch and vehicle energy consumption under state-of-charge (SOC) and charger-capacity constraints, explicitly quantifying vehicle–crane alignment at both handling interfaces. Secondly, an enhanced multi-objective algorithm (ST-NSGA-II) is developed, integrating a feasibility-preserving recursive decoding mechanism and a spatio-temporal variable neighborhood search (VNS) procedure. Finally, numerical experiments demonstrate that ST-NSGA-II significantly reduces mismatch and energy consumption compared to standard NSGA-II in large-scale scenarios. It also outperforms MOEA/D in Pareto-set quality, yielding a higher hypervolume (1.301 vs. 0.960) and a lower Spacing value (0.102 vs. 0.185). The results demonstrate that the proposed spatio-temporal optimization approach can effectively reduce handover mismatch compared to conventional scheduling modes, thereby achieving seamless task scheduling for vehicle–crane coordination. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

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19 pages, 6028 KB

Open AccessArticle

Multi-View Point Cloud Registration Method for Automated Disassembly of Container Twist Locks

by Chao Mi, Teng Wang, Xintai Man, Mengjie He, Zhiwei Zhang and Yang Shen

J. Mar. Sci. Eng. 2026, 14(7), 605; https://doi.org/10.3390/jmse14070605 - 25 Mar 2026

Cited by 1 | Viewed by 669

Abstract

With the continuous expansion of maritime trade scale, ports have put forward increasingly higher requirements for transshipment efficiency. Container twist lock disassembly is a key link in the loading and unloading process, and its automation level has a significant impact on the ship’s berthing time at the port. Aiming at the demand of automated disassembly for high-precision 3D vision, this paper proposes a multi-view point cloud local registration method for twist lock recognition. First, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is used to extract the keyhole region with the highest overlap in multi-view point clouds, reducing the interference from non-overlapping structures. Then, a two-stage strategy of “coarse registration + fine registration” is adopted: initial alignment is achieved through Random Sample Consensus (RANSAC), and the Iterative Closest Point (ICP) algorithm is improved by combining adaptive distance threshold and normal consistency constraint to complete fine registration. Experimental results show that the proposed method outperforms the global registration scheme in both accuracy and efficiency: the Root Mean Square Error (RMSE) is reduced to 2.15 mm, the Relative Mean Distance (RMD) is reduced to 1.81 mm, and the registration time is approximately 2.41 s. Compared with global registration, the efficiency is improved by 44.2%, which can meet the real-time requirements of continuous operation at automated terminals for the perception link and the time constraints for subsequent manipulator control. The research results preliminarily verify the application potential of this method in the scenario of automated twist lock disassembly. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

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