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Smart Seaport and Maritime Transport Management
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Smart Seaport and Maritime Transport Management

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

Deadline for manuscript submissions: 25 January 2025 | Viewed by 11336

Special Issue Editors

Dr. Lingxiao Wu

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: maritime studies; data-driven decision-making; machine learning; large-scale optimization; optimization under uncertainty; production and logistics operations
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuaian Wang

E-Mail Website
Guest Editor
Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: shipping network design; port operations planning; urban transport network modeling; sustainable transportation and green logistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in technology have revolutionized various industries, and the maritime sector is no exception. Smart seaports and maritime transport management systems have emerged as key drivers of efficiency, sustainability, and safety in the global shipping industry. These intelligent systems leverage cutting-edge technologies, such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, operations research, and automation, to optimize port operations, enhance vessel management, and streamline supply chain logistics.

This Special Issue aims to gather state-of-the-art research and advancements in the field of smart seaport and maritime transport management. Ultimately, it will provide a platform for researchers, academicians, and industry professionals to present their original research, case studies, and innovative solutions addressing the challenges, opportunities, and best practices associated with the implementation and operation of intelligent systems in maritime environments.

Papers are invited on various aspects related to smart seaport and maritime transport management. Potential topics include, but are not limited to:

  • Intelligent port infrastructure and automation systems;
  • IoT-enabled vessel monitoring and management;
  • Data-driven decision support systems for port and shipping operations;
  • Predictive maintenance and condition monitoring of maritime assets;
  • Big data analytics for optimizing cargo handling and logistics;
  • Blockchain applications in maritime supply chain management;
  • Cybersecurity and risk management in smart seaports;
  • Environmental sustainability and energy efficiency in maritime operations;
  • Integration of AI and machine learning in port and vessel operations;
  • Autonomous vessels and unmanned systems in maritime transport.

Dr. Lingxiao Wu
Prof. Dr. Shuaian Wang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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

  • port operations
  • shipping management
  • operations research
  • data analytics

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Published Papers (10 papers)

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Research

24 pages, 3066 KiB  
Article
Advancing Ton-Bag Detection in Seaport Logistics with an Enhanced YOLOv8 Algorithm
by Xiulin Qiu, Haozhi Zhang, Chang Yuan, Qinghua Liu and Hongzhi Yao
J. Mar. Sci. Eng. 2024, 12(11), 1916; https://doi.org/10.3390/jmse12111916 - 27 Oct 2024
Viewed by 556
Abstract
Intelligent logistics and freight transportation is an important part of realizing the intelligence of port terminals. Due to the problems of inaccurate ton bag identification, high costs, large model sizes, and long computation times in traditional freight transportation—issues that hinder meeting real-time requirements [...] Read more.
Intelligent logistics and freight transportation is an important part of realizing the intelligence of port terminals. Due to the problems of inaccurate ton bag identification, high costs, large model sizes, and long computation times in traditional freight transportation—issues that hinder meeting real-time requirements on resource-constrained operational equipment—this paper proposes an improved lightweight ton bag detection algorithm, YOLOv8-TB (YOLOv8-Ton Bag), which is optimized based on YOLOv8. Firstly, the improved LZKAC module is introduced to combine with SPPF to form a new SPPFLKZ module, which improves the feature expression performance. Then, with reference to spatial and channel reconstruction convolution and deformable convolution, the C2f-SCTT block is designed for the backbone network, which reduces the spatial and channel redundancy between features in the network. Finally, the C2f-ORECZ block based on a linear scaling layer is designed for the neck, which reduces the training overhead and strengthens the feature learning of the feature extraction network for the targets in the complex background of the harbor and adds the 160 × 160 scale detection head to strengthen small target detection abilities. On the logistics ton bag operation dataset provided by shipping port enterprises, the improved algorithm improves by 3.7% and 5% compared with the original algorithm in mAP50 and mAP50-95, respectively, the model size is reduced by 4.42 MB and the amount of model computation is only 8 G, which is capable of accurately detecting logistics ton bags in real time. The superiority of the method is verified by comparing it with other classical target detection algorithms. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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26 pages, 4275 KiB  
Article
Interpretable Machine Learning: A Case Study on Predicting Fuel Consumption in VLGC Ship Propulsion
by Aleksandar Vorkapić, Sanda Martinčić-Ipšić and Rok Piltaver
J. Mar. Sci. Eng. 2024, 12(10), 1849; https://doi.org/10.3390/jmse12101849 - 16 Oct 2024
Viewed by 831
Abstract
The integration of machine learning (ML) in marine engineering has been increasingly subjected to stringent regulatory scrutiny. While environmental regulations aim to reduce harmful emissions and energy consumption, there is also a growing demand for the interpretability of ML models to ensure their [...] Read more.
The integration of machine learning (ML) in marine engineering has been increasingly subjected to stringent regulatory scrutiny. While environmental regulations aim to reduce harmful emissions and energy consumption, there is also a growing demand for the interpretability of ML models to ensure their reliability and adherence to safety standards. This research highlights the need to develop models that are both transparent and comprehensible to domain experts and regulatory bodies. This paper underscores the importance of transparency in machine learning through a use case involving a VLGC ship two-stroke propulsion engine. By adhering to the CRISP-DM standard, we fostered close collaboration between marine engineers and machine learning experts to circumvent the common pitfalls of automated ML. The methodology included comprehensive data exploration, cleaning, and verification, followed by feature selection and training of linear regression and decision tree models that are not only transparent but also highly interpretable. The linear model achieved an RMSE of 23.16 and an MRAE of 14.7%, while the accuracy of decision trees ranged between 96.4% and 97.69%. This study demonstrates that machine learning models for predicting propulsion engine fuel consumption can be interpretable, adhering to regulatory requirements, while still achieving adequate predictive performance. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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29 pages, 4329 KiB  
Article
Contribution of Onshore Power Supply (OPS) and Batteries in Reducing Emissions from Ro-Ro Ships in Ports
by Ana B. Albo-López, Camilo Carrillo and Eloy Díaz-Dorado
J. Mar. Sci. Eng. 2024, 12(10), 1833; https://doi.org/10.3390/jmse12101833 - 14 Oct 2024
Viewed by 956
Abstract
Increasingly restrictive environmental regulations for the maritime sector have led shipping companies to look for technological alternatives to reduce emissions. This article introduces a methodology to analyse emission reductions of ships in port by incorporating batteries into the ships or using an onshore [...] Read more.
Increasingly restrictive environmental regulations for the maritime sector have led shipping companies to look for technological alternatives to reduce emissions. This article introduces a methodology to analyse emission reductions of ships in port by incorporating batteries into the ships or using an onshore power supply system. These have not yet been considered together for comparison or with a focus on ship operation. The aim is to avoid the use of auxiliary engines in ports. First, the cost calculation method to be used is specified; then, the engine’s behaviour and the established basic navigation criteria are analysed; and finally, different alternatives are considered. A methodology is afterwards defined for selecting alternatives, comparing their costs with those of using auxiliary engines in port. As an example, it is applied to a Ro-Ro route between the ports of Montoir (France) and Vigo (Spain). The results indicate that incorporating batteries into the ship produces greater savings in annual costs than onshore power supply. The cost savings from onshore power supply depend on the range of prices in each port. However, the greatest emission savings are obtained by using the onshore power supply. This methodology can be extrapolated to other routes and vessels by incorporating real operating data. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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18 pages, 1308 KiB  
Article
Assessing Critical Entities: Risk Management for IoT Devices in Ports
by Ioannis Argyriou and Theocharis Tsoutsos
J. Mar. Sci. Eng. 2024, 12(9), 1593; https://doi.org/10.3390/jmse12091593 - 9 Sep 2024
Cited by 1 | Viewed by 888
Abstract
Integrating Internet of Things (IoT) devices into port operations has brought substantial improvements in efficiency, automation, and connectivity. However, this technological advancement has also introduced new operational risks, particularly in terms of cybersecurity vulnerabilities and potential disruptions. The primary objective of this scientific [...] Read more.
Integrating Internet of Things (IoT) devices into port operations has brought substantial improvements in efficiency, automation, and connectivity. However, this technological advancement has also introduced new operational risks, particularly in terms of cybersecurity vulnerabilities and potential disruptions. The primary objective of this scientific article is to comprehensively analyze and identify the primary security threats and vulnerabilities that IoT devices face when deployed in port environments. This includes examining potential risks, such as unauthorized access, cyberattacks, malware, etc., that could disrupt critical port operations and compromise sensitive information. This research aims to assess the critical entities associated with IoT devices in port environments and develop a comprehensive risk-management framework tailored to these settings. It also aims to explore and propose strategic measures and best practices to mitigate these risks. For this research, a risk-management framework grounded in the principles of ORM, which includes risk avoidance, reduction, sharing, and retention strategies, was developed. The primary outcome of this research is the development of a comprehensive risk-management framework specifically tailored for IoT devices in port environments, utilizing Operational Risk-Management (ORM) methodology. This framework will systematically identify and categorize critical vulnerabilities and potential threats for IoT devices. By addressing these objectives, the article seeks to provide actionable insights and guidelines that can be adopted by port authorities and stakeholders to safeguard their IoT infrastructure and maintain operational stability in the face of emerging threats. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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21 pages, 1531 KiB  
Article
Joint Ship Scheduling and Speed Optimization for Naval Escort Operations to Ensure Maritime Security
by Xizi Qiao, Ying Yang, Yong Jin and Shuaian Wang
J. Mar. Sci. Eng. 2024, 12(8), 1454; https://doi.org/10.3390/jmse12081454 - 22 Aug 2024
Viewed by 892
Abstract
Maritime transport is crucial for global trade, as over 80% of goods are transported by sea. Recent conflicts have exposed the vulnerability of shipping routes to disruptions. Therefore, devising an optimal plan for naval escort operations is critical to ensure that ships are [...] Read more.
Maritime transport is crucial for global trade, as over 80% of goods are transported by sea. Recent conflicts have exposed the vulnerability of shipping routes to disruptions. Therefore, devising an optimal plan for naval escort operations is critical to ensure that ships are safely escorted. This study addresses the naval escort operation problem by constructing a mixed-integer programming model that integrates escort scheduling of the warship with the speed optimization of liner ships, aiming to minimize overall cargo delay and fuel consumption costs while ensuring the protection of all ships. The results indicate that as the number of container ships increases, ships wait longer before departure with the warship, leading to a higher average delay cost per ship. For instances with a single ship type, ships have similar sailing speeds on different legs. The proposed model balances cargo delivery timeliness with carbon emission reduction, enhancing economic viability and environmental sustainability in crisis-prone maritime scenarios. Future research should explore real-time data integration and adaptive strategies to improve naval escort operations’ robustness and responsiveness. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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16 pages, 2982 KiB  
Article
Spatial Structure and Vulnerability of Container Shipping Networks: A Case Study in the Beibu Gulf Sea Area
by Mengyu Xia, Jinhai Chen, Pengfei Zhang, Peng Peng and Christophe Claramunt
J. Mar. Sci. Eng. 2024, 12(8), 1307; https://doi.org/10.3390/jmse12081307 - 2 Aug 2024
Viewed by 1045
Abstract
Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea area is a key channel to supporting China’s participation in [...] Read more.
Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea area is a key channel to supporting China’s participation in international economic cooperation in the western region. It is highly susceptible to the influence of the political and economic instability. This study introduces a dual-component framework to analyze the inherent structure and potential vulnerabilities of the container transportation network in the Beibu Gulf Sea areas. The findings show that the core layer of the network exhibited circular solidification characteristics. The entire network heavily relies on some core ports, such as Haiphong Port, Ho Chi Minh Port, and Qinzhou Port, and it highlights the potential increases in vulnerability. The finding shows that deliberate attacks have a greater impact than random attacks on the normal operations of maritime networks. If ports with high intermediary centrality are attacked, the connectivity and transportation efficiency of the Beibu Gulf maritime network will be significantly affected. However, under such circumstances, redistributing cargo transportation through route adjustments can deal with the transmission of cascading failures and maintain the network’s resilience. Based on the existing knowledge and the data collected in a case study, this research stands out as the first to provide a critical examination of the spatial structure and vulnerability of container shipping networks in the Beibu Gulf sea. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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21 pages, 4048 KiB  
Article
Unveiling the Sensitivity Analysis of Port Carbon Footprint via Power Alternative Scenarios: A Deep Dive into the Valencia Port Case Study
by Seyed Behbood Issa-Zadeh, M. Dolores Esteban, José-Santos López-Gutiérrez and Claudia Lizette Garay-Rondero
J. Mar. Sci. Eng. 2024, 12(8), 1290; https://doi.org/10.3390/jmse12081290 - 31 Jul 2024
Viewed by 847
Abstract
The Port of Valencia, a prominent maritime center, is actively working towards minimizing its carbon emissions and aims to become a completely carbon-neutral port soon. This research uses data-driven sensitivity analysis to explore realistic power-generating options for a seaport to reduce its emissions. [...] Read more.
The Port of Valencia, a prominent maritime center, is actively working towards minimizing its carbon emissions and aims to become a completely carbon-neutral port soon. This research uses data-driven sensitivity analysis to explore realistic power-generating options for a seaport to reduce its emissions. This approach comprises changing key parameters in power consumption and deploying renewable energies (rather than electricity and infrastructure prices, which are beyond the scope of this study) to assess their impact on the port’s overall emissions profile. Through sensitivity analysis, policymakers and managers discover each scenario’s efficacy and find the best decarbonization strategies. After thoroughly examining four realistic scenarios, our research findings show that each scenario’s emission reduction share and sensitivity are practical and feasible. It becomes clear that gradually replacing traditional fossil fuels for electricity generation with renewables is a reasonable and realistic option for emissions reduction. The results demonstrate that focusing on reasonable targets, such as replacing 30% and 50% of electricity generation with renewables, is more achievable and beneficial in the medium term than ambitious goals, like replacing all electricity with renewable energy. This research contributes to reducing emissions of the Port of Valencia by using data-driven sensitivity analysis to find practical renewable energy strategies. It provides actionable insights for managers and policymakers to implement feasible decarbonization plans, emphasizing gradual adoption of renewables over ambitious goals, thus supporting sustainable maritime operations. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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24 pages, 3752 KiB  
Article
Optimization of Joint Scheduling for Automated Guided Vehicles and Unmanned Container Trucks at Automated Container Terminals Considering Conflicts
by Liangyong Chu, Zijian Gao, Shuo Dang, Jiawen Zhang and Qing Yu
J. Mar. Sci. Eng. 2024, 12(7), 1190; https://doi.org/10.3390/jmse12071190 - 16 Jul 2024
Viewed by 1081
Abstract
Port development is a critical component in constructing a resilient transportation infrastructure. The burgeoning integration of automated guided vehicles (AGVs) within container terminals, in conjunction with the orchestrated scheduling of unmanned container trucks (UCTs), is essential for the sustainable expansion of port operations [...] Read more.
Port development is a critical component in constructing a resilient transportation infrastructure. The burgeoning integration of automated guided vehicles (AGVs) within container terminals, in conjunction with the orchestrated scheduling of unmanned container trucks (UCTs), is essential for the sustainable expansion of port operations in the future. This study examined the influence of AGVs in automated container terminals and the synergistic scheduling of UCTs on port operations. Comparative experiments were meticulously designed to evaluate the feasibility of integrated scheduling schemes. Through the development of optimization models that consider conflict-free paths for both AGVs and UCTs, as well as strategies for conflict resolution, a thorough analysis was performed. Advanced genetic algorithms were engineered to address task-dispatching models. In contrast, the A* optimization search algorithm was adapted to devise conflict-free and conflict-resolution paths for the two vehicle types. A range of scaled scenarios was utilized to assess the impact of AGVs and UCTs on the joint-scheduling process across various configuration ratios. The effectiveness of the strategies was appraised by comparing the resultant path outcomes. Additionally, comparative algorithmic experiments were executed to substantiate the adaptability, efficacy, and computational efficiency of the algorithms in relation to the models. The experimental results highlight the viability of tackling the joint-scheduling challenge presented by AGVs and UCTs in automated container terminals. When juxtaposed with alternative scheduling paradigms that operate independently, this integrated approach exhibits superior performance in optimizing the total operational costs. Consequently, it provides significant insights into enhancing port scheduling practices. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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20 pages, 1367 KiB  
Article
Liner Schedule Design under Port Congestion: A Container Handling Efficiency Selection Mechanism
by Haibin Qu, Xudong Wang, Lingpeng Meng and Chuanfeng Han
J. Mar. Sci. Eng. 2024, 12(6), 951; https://doi.org/10.3390/jmse12060951 - 5 Jun 2024
Cited by 3 | Viewed by 1257
Abstract
Port congestion significantly impacts the reliability of container ship schedules. However, the existing research often treats vessel time in port as a random variable, failing to systematically consider the complex impact of port congestion on ship schedules. This study addresses the issue of [...] Read more.
Port congestion significantly impacts the reliability of container ship schedules. However, the existing research often treats vessel time in port as a random variable, failing to systematically consider the complex impact of port congestion on ship schedules. This study addresses the issue of container ship schedule design under port congestion. Vessel waiting times in ports are predicted and quantified by queueing theory, along with information on vessel schedules, cargo handling volumes, and available port operating time windows. We propose a mechanism for selecting container handling efficiencies for arriving vessels, thereby determining their in-port handling times. By jointly considering the uncertainty of vessel waiting and handling times in port, we establish a mixed-integer nonlinear programming model aimed at minimizing the total cost of liner transportation services. We linearize the model and solve it using CPLEX, ultimately devising a robust ship schedule. A simulation analysis is conducted on a real liner shipping route from Asia to the Mediterranean, revealing that extreme weather events, geopolitical conflicts, and other factors can lead to severe congestion at certain ports, necessitating timely adjustments to vessel schedules by shipping companies. Moreover, such events can impact the marine fuel market, prompting shipping companies to adopt strategies such as increasing vessel numbers and reducing vessel speeds in response to high fuel prices. Additionally, the container handling efficiency selection mechanism based on information sharing enables shipping companies to flexibly design liner schedules, balancing the economic costs and service reliability of container liner transportation. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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23 pages, 1778 KiB  
Article
Optimization of Berth-Tug Co-Scheduling in Container Terminals under Dual-Carbon Contexts
by Yan Wang and Tianyu Zou
J. Mar. Sci. Eng. 2024, 12(4), 684; https://doi.org/10.3390/jmse12040684 - 21 Apr 2024
Cited by 1 | Viewed by 1448
Abstract
In order to address the dynamic changes in vessel preferences for berth lines caused by the deployment of shore-based power equipment in major ports and the collaborative scheduling problem of berthing and towing assistance, this paper quantifies the environmental costs of pollutants from [...] Read more.
In order to address the dynamic changes in vessel preferences for berth lines caused by the deployment of shore-based power equipment in major ports and the collaborative scheduling problem of berthing and towing assistance, this paper quantifies the environmental costs of pollutants from the main engines of tugs and auxiliary engines of container ships using an environmental tax. Additionally, considering the economic costs such as vessel delay and shore power cable connection, a two-layer mixed-integer linear programming model is constructed using the task sequence mapping method. This model integrates the allocation of continuous berths at container terminals with coordinated towing scheduling for shore power selection. A solution approach is designed by combining the commercial solver (CPLEX) and the immune particle swarm optimization algorithm (IAPSO). The proposed scheme is validated using the example of the Nansha Phase IV Terminal at the Port of Guangzhou. The results show that compared to the traditional first-come-first-served and adjacent scheduling schemes, the collaborative scheduling scheme proposed in this paper reduces the total cost by 21.73%. By effectively utilizing berth resources and shore power equipment while densely arranging collaborative tasks and appropriately increasing the number of tugs, the port can convert the economic cost of leasing a small number of tugs (increased by 10.63%) into environmental benefits (decreased by 33.88%). This approach provides a reference for addressing nearshore pollution emissions in ports. Full article
(This article belongs to the Special Issue Smart Seaport and Maritime Transport Management)
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