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Intelligent Solutions for Marine Operations
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Intelligent Solutions for Marine Operations

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 January 2026 | Viewed by 2410

Special Issue Editors

Dr. Christian Velasco-Gallego

E-Mail Website
Guest Editor
Grupo de Investigación ARIES, Universidad Nebrija, 28015 Madrid, Spain
Interests: marine machinery; smart maintenance; smart transportation; applied artificial intelligence; operations research; smart supply chain; sustainability research
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Leszek Chybowski

E-Mail Website
Guest Editor
Faculty of Marine Engineering, Maritime University of Szczecin, 70-500 Szczecin, Poland
Interests: diesel engines; operation and maintenance; reliability and safety; energy conversion; failure prevention; directed innovations; marine systems; machinery; propulsion systems; TRIZ; inventics; modelling; fault prediction; condition monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The maritime industry is currently undergoing a digital transformation that is revolutionising marine operations by introducing intelligent solutions powered by cutting-edge technologies. These technologies are driving the integration of the Internet of Ships and digital engineering, including digital twins, Artificial Intelligence (AI), big data, and cloud computing, into the maritime environment to enhance efficiency and innovation in marine operations.

This Special Issue aims to explore original research on the development and application of intelligent solutions for maritime operations, introducing novel contributions in areas such as predictive maintenance, cyber–physical systems, autonomous and unmanned marine systems, AI-accelerated computational fluid dynamics, and green maritime technologies. Authors are invited to submit original research and development work that advances intelligent solutions for marine operations.

The scope of this Special Issue includes, but is not limited to, topics such as the following:

  • Application of Artificial Intelligence in marine operations. Predictive maintenance of vessels and offshore structures, intelligent weather forecasting, and route optimisation.
  • Internet of Ships. Smart sensors and real-time monitoring, digital twins, cyber–physical systems in marine operations, and cybersecurity.
  • Smart applications in autonomous and unmanned marine systems. AI-powered autonomous surface and underwater vehicles, collision avoidance, and path optimisation algorithms.
  • AI-accelerated computational fluid dynamics. Hybrid AI-CFD models for ship design and real-time performance optimisation.
  • Green maritime technologies. Data-driven models for fuel efficiency optimisation and emissions reduction, and renewable energy integration in marine operations.

Dr. Christian Velasco-Gallego
Prof. Dr. Leszek Chybowski
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

  • Artificial Intelligence
  • predictive maintenance
  • Internet of Ships
  • smart sensors
  • digital twins
  • cyber–physical systems
  • computational fluid dynamics
  • green maritime technologies
  • maritime autonomous surface ships
  • fuel efficiency optimisation

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

Published Papers (4 papers)

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Research

20 pages, 6757 KiB  
Article
FLUID: Dynamic Model-Agnostic Federated Learning with Pruning and Knowledge Distillation for Maritime Predictive Maintenance
by Alexandros S. Kalafatelis, Angeliki Pitsiakou, Nikolaos Nomikos, Nikolaos Tsoulakos, Theodoros Syriopoulos and Panagiotis Trakadas
J. Mar. Sci. Eng. 2025, 13(8), 1569; https://doi.org/10.3390/jmse13081569 - 15 Aug 2025
Viewed by 343
Abstract
Predictive maintenance (PdM) is vital to maritime operations; however, the traditional deep learning solutions currently offered heavily depend on centralized data aggregation, which is impractical under the limited connectivity, privacy concerns, and resource constraints found in maritime vessels. Federated Learning addresses privacy by [...] Read more.
Predictive maintenance (PdM) is vital to maritime operations; however, the traditional deep learning solutions currently offered heavily depend on centralized data aggregation, which is impractical under the limited connectivity, privacy concerns, and resource constraints found in maritime vessels. Federated Learning addresses privacy by training models locally, yet most FL methods assume homogeneous client architectures and exchange full model weights, leading to heavy communication overhead and sensitivity to system heterogeneity. To overcome these challenges, we introduce FLUID, a dynamic, model-agnostic FL framework that combines client clustering, structured pruning, and student–teacher knowledge distillation. FLUID first groups vessels into resource tiers and calibrates pruning strategies on the most capable client to determine optimal sparsity levels. In subsequent FL rounds, clients exchange logits over a small reference set, decoupling global aggregation from specific model architectures. We evaluate FLUID on a real-world heavy-fuel-oil purifier dataset under realistic heterogeneous deployment. With mixed pruning across clients, FLUID achieves a global R2 of 0.9352, compared with 0.9757 for a centralized baseline. Predictive consistency also remains high for client-based data, with a mean per-client MAE of 0.02575 ± 0.0021 and a mean RMSE of 0.0419 ± 0.0036. These results demonstrate FLUID’s ability to deliver accurate, efficient, and privacy-preserving PdM in heterogeneous maritime fleets. Full article
(This article belongs to the Special Issue Intelligent Solutions for Marine Operations)
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30 pages, 3877 KiB  
Article
Ship Voyage Route Waypoint Optimization Method Using Reinforcement Learning Considering Topographical Factors and Fuel Consumption
by Juhyang Lee, Youngseo Park, Jeongon Eom, Hungyu Hwang and Sewon Kim
J. Mar. Sci. Eng. 2025, 13(8), 1554; https://doi.org/10.3390/jmse13081554 - 13 Aug 2025
Viewed by 345
Abstract
As the IMO and the EU strengthen carbon emission regulations, eco-friendly voyage planning is increasingly recognized by ship owners as one of the most important performance factors of the vessel fleet. The eco-friendly voyage planning aims to reduce carbon emissions and fuel consumption [...] Read more.
As the IMO and the EU strengthen carbon emission regulations, eco-friendly voyage planning is increasingly recognized by ship owners as one of the most important performance factors of the vessel fleet. The eco-friendly voyage planning aims to reduce carbon emissions and fuel consumption while satisfying voyage constraints. In this study, a novel route waypoint optimization method is proposed, which combines a fuel consumption forecasting model based on the Transformer and a Proximal Policy Optimization (PPO) algorithm for adaptive waypoint planning. The developed framework suggests a multi-objective methodology unlike the traditional approaches where a single objective is sought after, which characterizes fuel efficiency against navigational safety and operational simplicity. The methodology consists of three sequential phases. First, the transformer model is employed to predict ship fuel consumption using navigational and environmental data. Next, the predicted consumption values are utilized as a reward function in a PPO-based reinforcement learning framework to generate fuel-efficient routes. Finally, the number and placement of waypoints are further optimized with respect to terrain and bathymetric constraints, improving the practicality and safety of the navigational plan. The results show that the proposed method could decrease average fuel consumption by up to 11.33% across three real-world case studies: Busan–Rotterdam, Busan–Los Angeles, and Mokpo–Houston, compared to AIS-based routes. The transformer model outperformed Long Short-Term Memory (LSTM) and Random Forest baselines with the highest prediction accuracy, achieving an R2 score of 86.75%. This study is the first to incorporate transformer-based forecasting into reinforcement learning for maritime route planning and demonstrates how the method adaptively controls waypoint density in response to environmental and geographical conditions. These results support the practical application of the approach in smart ship navigation systems aligned with IMO’s decarbonization goals. Full article
(This article belongs to the Special Issue Intelligent Solutions for Marine Operations)
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23 pages, 28451 KiB  
Article
The Application of a Marine Weather Data Reconstruction Model Based on Deep Super-Resolution in Ship Route Optimization
by Shangfu Li, Junfu Yuan and Zhizheng Wu
J. Mar. Sci. Eng. 2025, 13(6), 1026; https://doi.org/10.3390/jmse13061026 - 23 May 2025
Viewed by 509
Abstract
Accurate weather data are very important for the navigation of ships. However, due to the insufficient coverage of the maritime network, the high cost of satellite communication, and the limited bandwidth, it is difficult for ships to obtain high-resolution weather data during route [...] Read more.
Accurate weather data are very important for the navigation of ships. However, due to the insufficient coverage of the maritime network, the high cost of satellite communication, and the limited bandwidth, it is difficult for ships to obtain high-resolution weather data during route planning. This challenge greatly limits the accuracy and effectiveness of ship navigation. To solve this problem, this paper proposes a marine weather data reconstruction model based on deep super-resolution. Firstly, the model uses a convolutional neural network to extract features from wind speed and wave height data. Secondly, the model uses SRResNet as the reconstruction framework and effectively captures the complex nonlinear feature relationship in weather data through the residual block structure to realize the fine reconstruction of low-resolution weather data. In addition, the attention mechanism is integrated into the model to dynamically adjust the weights of different weather features, which further enhances the attention to key features. The results show that the model has a good effect on the super-resolution reconstruction of weather data. The PSNR, SSIM, GMSD, and FSIM of wave height reconstruction are 49.73 dB, 0.9949, 0.0082, and 0.9999, respectively, and the PSNR, SSIM, GMSD, and FSIM of wind speed reconstruction are 41.52 dB, 0.9797, 0.0400, and 0.9997, respectively. Based on the reconstructed data, route planning can effectively reduce the navigation distance of the ship and avoid unnecessary detours, thus saving fuel consumption and reducing operating costs. Full article
(This article belongs to the Special Issue Intelligent Solutions for Marine Operations)
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18 pages, 8566 KiB  
Article
Machine Learning-Based Mooring Failure Detection for FPSOs: A Two-Step ANN Approach
by Omar Jebari, Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin and Moohyun Kim
J. Mar. Sci. Eng. 2025, 13(4), 791; https://doi.org/10.3390/jmse13040791 - 16 Apr 2025
Viewed by 742
Abstract
This study presents a two-step artificial neural network (ANN) approach for detecting mooring failures in a spread-moored floating production storage and offloading (FPSO) vessel using platform motion data. Synthetic statistical data generated from time-domain simulations were utilized as input features. The first-step ANN [...] Read more.
This study presents a two-step artificial neural network (ANN) approach for detecting mooring failures in a spread-moored floating production storage and offloading (FPSO) vessel using platform motion data. Synthetic statistical data generated from time-domain simulations were utilized as input features. The first-step ANN determines whether the mooring system is intact or a failure has occurred within a specific mooring group. If a failure is detected, the second-step ANN identifies the exact failed mooring line within the group. Hyperparameter optimization was performed using Bayesian and random search methods, and multiple input variable sets were evaluated. The results indicate that the mean values of platform motions, particularly surge and yaw, play a crucial role in accurately identifying mooring failures. Additionally, selecting the top 10 features based on mutual information can be a way to improve detection accuracy. The proposed two-step ANN approach outperformed the single-step ANN method, achieving higher classification accuracy and reducing misclassification between mooring lines. These findings demonstrate the potential of machine learning for near-real-time mooring integrity monitoring, offering a practical and efficient alternative to traditional inspection methods. Full article
(This article belongs to the Special Issue Intelligent Solutions for Marine Operations)
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