To address the altitude-constrained path planning problem for Unmanned Underwater Vehicles (UUVs) in complex terrain environments, this paper proposes an improved hybrid A^* path planning algorithm based on UUV motion characteristics, termed UUVA^*. Firstly, the traditional hybrid A^* algorithm is extended by expanding the state space and incorporating the nonholonomic motion constraints of UUVs. Secondly, motion primitives tailored to UUV navigation characteristics are designed, and the cost function is optimized to generate smoother and more navigable paths. Finally, to enhance search efficiency, a multi-resolution hierarchical search strategy and branch pruning mechanism are introduced, significantly reducing the number of node expansions. Simulation tests conducted in two representative complex terrains—submarine hills, and submarine canyons—verify the altitude-constrained planning capability of the proposed algorithm. Furthermore, comparative experiments against classical A^* algorithm, hybrid A^*, and RRT^* algorithms are conducted. The result shows that the generated paths are smoother and yield smaller tracking errors, making the proposed algorithm more suitable for practical UUV applications, which verifies the superior performance of the proposed method in terms of trajectory tracking error and path navigability.

This study aims to improve the accuracy of ship motion prediction by proposing a gray-box modeling method for ship maneuvering motion, termed AE-LS-SVM, which combines the Alpha Evolution (AE) algorithm to optimize the Least Squares Support Vector Machine (LS-SVM). The AE algorithm's superior global search capability was employed to optimize the hyperparameters of LS-SVM. The dataset in this study was obtained through ship maneuvering tests conducted using the Marine Systems Simulator (MSS). Based on the Abkowitz model, a three-Degree-of-Freedom (3-DOF) gray-box model of ship maneuvering motion was constructed, incorporating maneuvering variables and ship responses. The gray-box model established in this study was then subjected to comparative analysis with gray-box models developed using GWO-LS-SVM (Grey Wolf Optimizer) and PSO-LS-SVM (Particle Swarm Optimization) through maneuvering tests. Experimental results demonstrate that the AE-LS-SVM method achieves reductions in Mean Squared Error (MSE) of approximately 6% and 4%, reductions in Mean Absolute Percentage Error (MAPE) of 3.84% and 1.96%, and improvements in correlation coefficient (CC) of 0.13% and 0.04%, respectively, compared to the two benchmark methods. Additionally, the anti-interference capability of the proposed method was validated by introducing varying levels of noise into the training dataset to simulate measurement errors. The findings indicate that the AE-LS-SVM method exhibits high modeling accuracy and robust noise resistance, offering valuable support for ship motion prediction.

With the development of intelligent shipping and autonomous ship maneuvering technologies, higher requirements were imposed on the parameter identification accuracy of ship motion models in practical navigation environments. However, under limited sample conditions, traditional parameter identification methods for nonlinear ship motion models were difficult to achieve both accuracy and stability, which restricted their engineering applications. To address this problem, a ship maneuvering parameter identification method based on Physics-Informed Neural Networks (PINN) was proposed. During network training, a second-order nonlinear ship maneuvering response model was deeply introduced as a physical prior constraint, and the dynamic information of the model was deeply integrated with limited observation data to improve the effectiveness of parameter learning under limited sample conditions. Numerical simulations of 20°/20° zigzag maneuvering tests were carried out based on the Mariner model ship, and training and validation datasets were constructed to identify ship maneuverability indices. The identification results were compared with those obtained by the Extended Kalman Filter (EKF) method and the Least-Squares Support Vector Machine (LS-SVM) method. The results show that, under limited sample conditions, the proposed method improves the identification accuracy of nonlinear ship maneuvering model parameters by 14.75% and 7.74% compared with the EKF method and the LS-SVM method, respectively. In robustness tests with Gaussian white noise contamination, stable parameter identification performance is maintained by the PINN-based method, and the minimum identification error reaches 0.05%. The results indicate that the proposed method effectively solves the problem of high-precision parameter identification for nonlinear ship maneuvering models under limited data conditions and provides a feasible technical approach for ship motion modeling and maneuvering performance analysis.

Reliable maritime situational awareness is a key requirement for the development of intelligent ships. Radar and the Automatic Identification System (AIS) are among the most widely deployed navigation sensors on board ships, and the association and fusion of their data provide the foundation for intelligent perception. However, ship state measurement data are often characterized by asynchronous sampling, missing observations, and uncertainty, which make track association challenging. To address this problem, this paper proposes a multi-target track association algorithm based on Dynamic Time Warping (DTW) and Dempster-Shafer Theory (DST). First, multi-source track data are temporally aligned and represented using the DTW method. Then, multidimensional track features are fused based on DST. A two-stage association decision strategy is further constructed to obtain the globally optimal matching of heterogeneous sensor data under constrained conditions. A multi-target track simulation dataset is established according to the observation characteristics of radar and AIS targets, and the applicability of the proposed algorithm is evaluated under different scenarios. The algorithm is further validated using sea-trial data collected from the intelligent research and training vessel Xin Hong Zhuan of Dalian Maritime University. The results show that the F₁-score of the proposed algorithm exceeds 0.9 in the track association task, while the performance fluctuation across different scenarios is only 9.03%. These results demonstrate that the proposed method has high accuracy and robustness, providing technical support for multi-source perception data fusion and maritime situational awareness in intelligent ships.

In the busy waters of nearshore port channels, physical Aids to Navigation (AtoN) are highly susceptible to collisions, which may cause structural damage and prevent them from operating normally. To address the research gap in autonomous navigation of AtoN, including the inability of existing methods to achieve autonomous navigation and collision avoidance and their insufficient integration with artificial intelligence technologies for intelligent navigation, this paper proposes an autonomous navigation method for AtoN based on improved Proximal Policy Optimization (PPO) combined with the Velocity Obstacle (VO) algorithm. The PPO algorithm was improved by introducing an offline learning strategy to enhance network update efficiency. By designing the autonomous navigation action space for AtoN, the model can achieve faster convergence. However, the model is prone to falling into local optima during convergence. To avoid this problem, the VO algorithm is incorporated to represent the state space, and a reward function is designed using velocity vectors. On the basis of calculating the distance-based reward function, an additional reward function based on position and velocity information is introduced to improve convergence speed and enable AtoN to adaptively select the optimal velocity in nearshore port channels. To further enhance algorithm robustness, an entropy reward term is added to the PPO algorithm to encourage greater exploration. Experimental results show that, compared with the method without the entropy reward term, the proposed method improves the average navigation step length and average navigation velocity by 9.3% and 10.2%, respectively, while maintaining a model test success rate of over 99%. The proposed method enables a safe operational mode featuring unmanned control, autonomous navigation, and collision avoidance for AtoN. It can reduce the collision damage risk of AtoN in busy waters and lower the costs of manual inspection and maintenance. The study is of significance for accelerating the construction of a modern maritime navigation support system and ensuring maritime navigation safety.

To address the challenge of coordinating and scheduling construction vessels in large-scale water projects such as waterway regulation, this problem is modeled for the first time as a Dual-Resource Berth Allocation Problem (DR-BAP). A mathematical model is established with the objective of minimizing vessel waiting costs and resource preference mismatch costs. To solve this Non-deterministic Polynomial-time hard (NP-hard) problem, a Two-Stage Hybrid Cuckoo Search Algorithm (TSH-CSA) is proposed. In the first stage, the algorithm employs dynamic priorities, a load balancing guidance strategy, and a historical congestion feedback penalty mechanism for macroscopic resource allocation. It integrates domain knowledge and feedback information to optimize fitness evaluation. In the second stage, the algorithm integrates an adaptive dual-population mechanism and an intelligent restart mechanism to achieve a microscopic, refined solution. Overall, the algorithm establishes a global iterative framework of "allocation-scheduling-feedback" to enhance its global optimization capability. Simulation experiments based on a waterway regulation project show that, compared with the standard Cuckoo Search Algorithm, TSH-CSA exhibits significant improvements in solution quality, convergence speed, and robustness. These results verify the effectiveness and advancement of the proposed model and algorithm in solving complex engineering scheduling problems.

With the continuous expansion of global trade and shipping, container throughput at terminals increases exponentially. Higher requirements are posed on port logistics efficiency, particularly the collaborative operation efficiency of automated guided vehicles (AGVs). Existing centralized algorithms and distributed algorithms exhibit limitations in balancing computational complexity and optimization quality. The demands of automated container terminals for shortening makespan and improving efficiency cannot be effectively accommodated by existing algorithms. To address these problems, a Hybrid task Allocation Algorithm (HAA) combining centralized algorithms and distributed bid was proposed. First, a multi-objective optimization model was constructed from the perspectives of the scheduling system, AGV fleet, and task set to minimize makespan, total AGV travel distance, and task execution delay. Second, a genetic algorithm was utilized to solve this problem. The task set was divided into ordered task subsets equal to the number of AGVs. Finally, bid values for the task subsets were distributively computed by each AGV. The cost matrix constructed from the bid values was solved using the Hungarian algorithm. Through simulation experiments, the proposed HAA method was validated under scenarios with various task scales and task densities. Comparative analyses with traditional centralized algorithm and distributed algorithms were performed. The results demonstrate that a solution quality close to that of centralized algorithms is achieved by the proposed method. Meanwhile, the computation time is reduced by 39.09% on average. Good and stable optimization performance is exhibited. Furthermore, the method can be effectively applied to periodic scheduling scenarios.

As the lifeblood of global trade, the international shipping industry accounts for approximately 3% of global carbon emissions, posing a significant challenge to the achievement of climate targets. Onboard carbon capture and storage (OCCS) technology is widely regarded as an important pathway for the shipping industry to move toward net-zero emissions because of its substantial emission-reduction potential and compatibility with existing vessels. This study focuses on the economic feasibility of OCCS technology and systematically analyzes its cost structure. The results reveal that initial capital expenditure and the additional fuel consumption caused by the capture system are the major economic barriers limiting its large-scale adoption. A quantitative case study of a medium-range (MR) tanker shows that, under specific operating and market conditions, the investment payback period of the OCCS system is 8.38 years, preliminarily demonstrating the economic potential of this technology for commercial application. Furthermore, the sensitivity analysis indicates that the carbon tax rate and the CO₂ market price are the decisive variables affecting the payback period, as well as key uncertainties that should be emphasized in future techno-economic assessments. Based on these findings, this study suggests that the commercialization of OCCS cannot rely solely on technological maturity, but requires a coordinated support framework integrating policy guidance, technological innovation, and market mechanisms. By providing empirical data, this study offers an analytical basis for translating the shipping industry's macro-level decarbonization strategy into micro-level economic decision-making and helps fill the gap in quantitative assessments of OCCS applications for specific ship types.

With the continuous enhancement of global shipping's requirements for energy conservation and emission reduction, accurate prediction of ship fuel consumption has become crucial for optimizing shipping efficiency and mitigating environmental impacts. However, the issues of feature redundancy and noise interference in multi-source data restrict the accuracy and stability of fuel consumption prediction models. To address these problems, a comprehensive framework for precise fuel consumption prediction is proposed. First, ship noon report data, ERA5 reanalysis data, global ocean physical analysis and prediction data, and Automatic Identification System (AIS) data are integrated, and a multi-dimensional feature space dataset is constructed. Second, to address feature redundancy and noise interference, a genetic algorithm is introduced for feature selection, and the original 32 features are reduced to 15. Third, nine machine learning regression models are systematically evaluated, and the results indicate that the Random Forest model exhibits significant advantages in capturing the nonlinear characteristics of fuel consumption. Finally, feature importance analysis based on the RF model reveals that time-related variables (such as daily operating hours and total operating hours) play a dominant role in fuel consumption prediction, providing quantitative decision support for energy efficiency management. The results show that the genetic algorithm-based feature selection effectively reduces computational complexity by approximately 53.1%, and the Random Forest model performs best in capturing the nonlinear characteristics of fuel consumption, significantly improving prediction accuracy. The research findings provide theoretical support for fuel optimization and emission reduction strategies in the shipping industry, and are of great significance in promoting the green transformation of maritime transport.

With the green transformation of port equipment, tugboat scheduling faces new opportunities and challenges. On the one hand, the deployment of new energy tugboats helps reduce port operating costs and carbon emissions; on the other hand, research on new energy tugboats remains relatively scarce. To address this research gap, a mixed-integer linear programming model for heterogeneous tugboat scheduling under the context of multiple port areas is established based on the characteristics of hybrid tugboats, aiming to minimize port costs, tugboat operation costs, and carbon emission costs. To solve the model, a Multi-Verse Optimization algorithm embedded with heuristic rules is applied, and its effectiveness and accuracy are verified through comparison with the Gurobi solver. In large-scale instances, the proposed algorithm achieved objective values that were, on average, 4% and 3.25% better than those obtained by the GA and IGA, respectively. The research results show that configuring high-power tugboats can effectively reduce ship port costs and tugboat operation costs. However, when the number of high-power tugboats is too large, it may increase operation costs, and at this point, appropriately configuring lower-power tugboats can leverage their low operation cost advantage. An appropriate configuration of a certain number of hybrid tugboats can effectively reduce the total scheduling cost as well as the carbon emission cost of tugboat operations. Meanwhile, the proportion of carbon emission costs in the overall operational cost can be continuously reduced from 8% to approximately 1.51%.

Existing link prediction methods for maritime shipping networks primarily rely on single-dimensional network structural information and fail to comprehensively account for multiple influencing factors. To address this limitation, a coupled similarity index integrating multi-level information is proposed. Specifically, to incorporate the effects of practical node attributes such as port operations, hinterland economic conditions, and maritime distances on link prediction, similarity indices based on node realistic attributes are constructed. The predictive performance of 22 individual similarity indices from four perspectives—local network structure, global network structure, quasi-local network structure, and node realistic attributes—is evaluated using the Area Under the Curve (AUC) metric. The index with the highest AUC value from each perspective is then selected. Subsequently, weighted averaging and grid search are employed to determine the optimal weight combination, thereby constructing the coupled similarity index with the highest predictive accuracy. Experimental results on the global container shipping network demonstrate that the Resource Allocation (RA), Random Walk with Restart (RWR), Superposed Random Walk (SRW), and Similarity based on Port Operations (SPO) indices achieve the best prediction performance within their respective perspectives. The proposed coupled similarity index significantly outperforms all individual indices. In particular, the relatively high weight assigned to the SPO index indicates that node realistic attributes play a dominant role in link prediction. Furthermore, comparison with newly added shipping routes in 2024 shows that the proposed method achieves a prediction accuracy of 45.48%, and the spatial distribution of the predicted routes is highly consistent with the global shipping network pattern, thereby validating the effectiveness of the proposed coupled similarity index.

Transshipment hub ports typically handle high-density water-to-water transshipment, while limited port resources pose significant challenges for efficient berth scheduling. To address this issue, a berth scheduling approach integrating multiple transshipment modes is proposed by incorporating a double-berthing operation, which allows vessels to berth side-by-side at the same berth and conduct direct transshipment via quay cranes. The double-berthing operation is further integrated with indirect transshipment and improved direct transshipment to form a coordinated transshipment framework. A mixed-integer programming model is formulated to jointly determine berth allocation and transshipment mode selection, with the objective of minimizing transshipment costs and vessel-related operational costs. The model incorporates practical constraints including transshipment time windows, vessel matching relationships, and quay crane width limitations. To solve the resulting complex optimization problem, an Adaptive Large Neighborhood Search (ALNS) algorithm is developed. Multiple destroy and repair operators together with an adaptive weight updating mechanism are designed to enhance the exploration capability and computational efficiency of the algorithm. Computational experiments of different scales are conducted, and the proposed algorithm is compared with the Gurobi solver, Genetic Algorithm (GA), and Simulated Annealing (SA). In addition, weight sensitivity analysis is performed to evaluate the robustness of the model under different cost weight settings. The applicability of the proposed approach is further verified using real arrival data from feeder berths at Shanghai Yangshan Port. The results show that, compared with traditional scheduling schemes, the proposed approach significantly reduces vessel waiting time and overall completion time while substantially decreasing truck transportation and yard storage costs. Specifically, the total vessel waiting time is reduced by 63.48%, the total operational time by 5.73%, and the overall operational cost by 65.09%. Furthermore, the improved ALNS achieves solutions within 3% of those obtained by Gurobi for small-and medium-sized instances, and obtains high-quality solutions within shorter computation times than GA and SA for large-scale instances, demonstrating strong robustness and computational efficiency.

Demand uncertainty and inland port congestion pose significant challenges to the efficient operation of port collection and distribution systems. This paper proposes a bi-level programming model that simultaneously account for demand uncertainty and inland port congestion. The upper-level model optimizes inland port location and capacity configuration by minimizing construction costs and congestion costs, while the lower-level model optimizes transport paths by minimizing transportation costs, transfer costs, carbon emission costs, and time costs. A Genetic Algorithm is employed to solve the model, with empirical analysis based on data from the Liaoning port cluster and its northeastern hinterland. The results show that higher-grade inland ports effectively absorb demand fluctuations and reduce congestion costs, while reducing carbon emissions by increasing the share of railway transport. A maximum regret value of θ=0.25 achieves the optimal balance between total cost and stability of the Liaoning network. Additionally, a carbon tax of ￥0.5/kg achieves the best emission reduction and congestion control outcomes.

This paper studies the routing design problem of inland river container liners. In accordance with the significant impact of loading volumes on the draft of inland ships, a strategy was proposed to optimize the last port of call on the ship's outbound route based on loading volumes. In view of the features that loaded containers can only be transported in one direction while empty containers can be transported in both directions on inland waterway, the node variables of empty containers were introduced. Then, two route design models considering cargo flow balance were established under two scenarios depending on whether the demand is divisible. Ten groups of real ships with different sizes on the Yangtze River were selected for example validation analysis. The results indicate that optimizing the last port of call for ships based on loading volumes and considering cargo flow balance are beneficial for improving the shipping company's voyage profits. Both the number of ships deployed on the route and the number of ports of call show a trend of first increasing and then remaining unchanged with the increase in the seasonal water depth of the Yangtze River channel or the decreases in the confidence level set by the shipping company. The shipping company's voyage profit also increases with water depth. The research conclusions can provide useful references for operation decision-making of shipping companies on inland waterway liner transportation.

This study analyzes the ultimate strength of an ultra-large container ship in accordance with the requirements of the International Association of Classification Societies (IACS) to assess the reliability of its hull structural ultimate strength.. A twin-island ultra-large container vessel is selected as the research object. Based on IACS UR S11A the ultimate strength of the ship is calculated, validated, and subjected to reliability analysis. Results indicate that ship may face a risk of insufficient strength under extreme operating conditions, and structural reinforcement is therefore required in accordance with UR S11A. By innovatively integrating the requirements of UR S11A with structural reliability theory, this study realizes a quantitative assessment of the ultimate strength for ultra-large container ships. The findings provide reference value for inspection and maintenance recommendations for aging vessels, as well as for route optimization.

With the vigorous growth of the shipping industry, ensuring navigational safety and improving operational efficiency have imposed higher demands on accurate wave simulations under extreme sea states. However, the applicability of the third-generation spectral wave model WAVEWATCH III (WW3) and the identification of optimal simulation configurations during the rapid intensification stage of super typhoons remain insufficient, limiting its ability to address two key challenges in typhoon-related maritime early warning: accurate simulation of typhoon-generated waves during the rapid intensification stage and precise classification of wave-induced disaster risk. To tackle these issues, a multi-dimensional sensitivity analysis framework is employed. First, typhoon characteristics and ship-accident information are compiled using the Northwest Pacific tropical cyclone best-track dataset provided by the China Meteorological Administration (CMA) and vessel accident investigation reports from the Guangdong Maritime Safety Administration. Second, high-resolution typhoon-wave simulation experiments are established using WW3. For source-term parameterizations, three widely used WW3 schemes (ST2, ST4, and ST6) are adopted. For atmospheric forcing, wind fields from the Weather Research and Forecasting (WRF) model, the fifth-generation ECMWF global atmospheric reanalysis (ERA5), and the NCEP Final Analysis (NCEP-FNL) are used to examine WW3 performance and to determine the optimal simulation configuration. Combined with observations from National Oceanic Administration buoys and standard statistical validation metrics, a complete technical workflow is developed. Under rapid intensification conditions of super typhoons, WW3 experiments with different source-term schemes and wind forcing fields are conducted and evaluated against buoy measurements. The results indicate that simulations driven by WRF winds and using the ST2 scheme achieve superior performance near the typhoon center, whereas the ST4 and ST6 schemes perform better in other regions.

As the world's largest Roll-on/Roll-off Passenger (Ro-Pax) terminal, Zhanjiang Xuwen Port faces severe vessel scheduling pressure during peak operation periods, placing higher requirements on scheduling efficiency. To address this issue, this paper, based on previous research, proposes an optimization method for the inbound and outbound sequence of Ro-Pax vessels by further considering constraints on dedicated berth for Ro-Ro vessels carrying dangerous goods and the characteristics of variable-speed vessel navigation. First, the inbound and outbound sequencing problem of Ro-Pax vessels is modeled as an Asymmetric Traveling Salesman Problem with Time Windows (ATSPTW). Then, constraints are formulated according to the navigation rules of the harbor basin and channel, and a weight matrix reflecting the minimum scheduling time intervals between vessels was constructed. Subsequently, a bi-objective dynamic programming model is established to minimize total scheduling time and total waiting time. Finally, a bi-objective Pareto optimization algorithm extended from the Held-Karp algorithm is developed to solve the model. The numerical experiment resulst based on actual operational data show that the optimized scheduling plan significantly outperforms the actual scheduling plan in terms of both total scheduling time and total waiting time, with optimization rates reaching 6.50% and 31.09%, respectively. The simulation experiments conducted using the FlexSim 3D simulation software further verify the safety and robustness of the optimized scheduling plan, with no violation of navigation rules observed throughout the entire operation process. The proposed optimization method can significantly improve scheduling efficiency and provide decision support for vessel scheduling at Xuwen Port and other similar ports during peak operation periods.

To address the challenge of simultaneously extracting local and global features in multi-task segmentation of Arctic sea ice, a hybrid architecture integrating residual networks and state space models was proposed in this paper. In this architecture, residual networks were employed as the encoder to extract multi-scale local features. Enhanced Visual State Space (EVSS) modules were adopted in the decoder to achieve global context modeling through the combination of 2D Selective Scan and dual attention mechanisms. In the skip connections, Cross-Scale Contextual Fusion (CSCF) modules were designed to integrate multi-level features from the encoder. Attention mechanisms were utilized to enhance the representation of key regions. In the training phase, a Decoder Stage-Wise Supervision (DSWS) was introduced to optimize the calculation of loss functions. Finally, sea ice concentration, development stages, and floe size predictions were generated simultaneously through parallel segmentation heads. Experiments on the AI4Arctic dataset demonstrate that the proposed model achieves a comprehensive score of 84.014. Its performance exceeds that of mainstream methods such as Swin Transformer and SegFormer in all three prediction tasks. Fine-grained segmentation of sea ice distribution is achieved in this paper. The results provide technical support for subsequent identification and delimitation of navigable water areas. The obtained segmentation results hold significant application value in Arctic route planning and ship navigation safety assessment.

Emergency anchoring operations pose a serious threat to the safety of subsea pipelines. Existing empirical formulas and theoretical models often show limited adaptability under complex geological conditions, particularly in characterizing the nonlinear evolution of soil strength during high-velocity penetration. To address this problem, this study takes the Hall anchor, which is widely used on merchant ships, as the research object and investigates the penetration behavior of ship emergency anchoring under static conditions using the Coupled Eulerian-Lagrangian (CEL) method. The Mohr-Coulomb constitutive model and the Einav strain-rate softening relationship are introduced to describe the dynamic degradation of soil strength. The anchor-soil contact interface is treated using the penalty function method, and an Eulerian domain with 586 000 elements is determined through a mesh convergence test. Taking the muddy clay in the waters of an offshore wind farm as the engineering background, the maximum penetration depths of emergency anchoring for a 20 000 GT LPG carrier and a 50 000 DWT oil tanker are calculated to be 2.908 m and 3.033 m, respectively. Compared with the DNV energy method and Young's formula, the proposed method can better reflect soil heterogeneity and large-deformation characteristics. The simulation results are further compared with experimental data, showing maximum errors of 13.1% in penetration depth and 16.1% in bottom-impact velocity, both of which are within the acceptable range for engineering applications. The results indicate that the proposed method has high reliability and is consistent with engineering practice, providing theoretical and technical support for subsea pipeline design, route planning, and anchoring risk control.