Operational efficiency is one of the key performance indicators of a port’s service level. In the process of making scheduling plans for container terminals, different types of equipment are usually scheduled separately. The interaction between quay cranes (QCs) and automated guided vehicles (AGVs) is neglected, which results in low operational efficiency. This research explores the integrated scheduling problem of QCs and AGVs. Firstly, a multi-objective mixed integer programming model (MOMIP) is conducted, with the aim of minimizing the makespan of vessels and the unladen time of AGVs. Then, embedded with a new heuristic method, the non-dominated sorting genetic algorithm-II (NSGA-II) is designed for the scheduling problem. The heuristic method includes two parts: a bay-based QC allocation strategy and a container-based QC-AGV scheduling strategy. Finally, in order to test the performance of the proposed algorithm, differently sized benchmark tests are performed, and the results are compared to the multi-objective particle swarm optimization algorithm (MOPSO) and the weighted-sum method. The computational results indicate that the proposed algorithm can effectively solve the multi-objective integrated scheduling problem of QCs and AGVs. For large-scale problems, the NSGA-II algorithm has better performance and more obvious advantages compared to others. The proposed method has the capability of providing a theoretical reference for the QC and AGV scheduling of container terminals.

To effectively address the increase in maritime accidents and the challenges posed by the trend toward larger ships for maritime safety, it is crucial to rationally allocate the limited maritime search and rescue (MSAR) resources and enhance accident response capabilities. We present a comprehensive method for allocating MSAR resources, aiming to improve the overall efficiency of MSAR operations. First, we use long short-term memory to predict the number of future accidents and employ the K-medoids algorithm to identify the accident black spots in the studied area. Next, we analyze the multi-constraint conditions in the MSAR resource allocation process. A multi-objective integer programming model is constructed to minimize the response time and allocation cost. Finally, we use the non-dominated sorting genetic algorithm II (DNSGA-II) with Deb’s rules to solve the model, and we propose a multi-attribute decision optimization-based method for MSAR resource allocation. We found that the DNSGA-II exhibits better convergence and generates higher-quality solutions compared to the NSGA-II, particle swarm optimization (PSO), and enhanced particle swarm optimization (EPSO) algorithms. Compared with the existing MSAR resource emergency response system, the optimized scheme reduces the response time and allocation cost by $11.32\%$ and $6.15\%$, respectively. The proposed method can offer decision makers new insights when formulating MSAR resource allocation plans.

In order to improve the accuracy of the freefall of lifeboat motion simulation in a ship life-saving simulation training system, a mathematical model using the strip theory and Kane’s method is established for the freefall of the lifeboat into the water from a ship. With the boat moving on a skid, the model of the ship’s maneuvering mathematical group (MMG) is used to model the motion of the ship in the waves. Based on the formula of elasticity and friction theory, the forces of the skid acting on the boat are calculated. When the boat enters the water, according to the analytical solution theory of slamming, the slamming force of water entry is solved. The simulation experiments are carried out by the established model. The results of the numerical simulation are compared with the calculation results of the hydrodynamics software Star CCM+ at water entry under initial condition A in the paper. The position and velocity of the center of gravity of the boat, the angle, and velocity and acceleration of pitch calculated by the two methods are in good agreement. There is a little difference between the values of translation acceleration calculated by the two methods, which is acceptable. This shows that our numerical algorithm has good accuracy. A qualitative analysis is performed to find the safe point of water entry under the condition of different wave heights and two situations of a ship encountering waves. Finally, the model is applied to the ship life-saving training system. The model can meet the system requirements and improve the accuracy of the simulation.

The widespread of shipborne Automatic Identification System (AIS) equipment will continue to produce a large amount of spatiotemporal trajectory data. In order to explore and understand the hidden behaviour patterns in the data, an interactive visual analysis method combining multiple views is proposed. The method mainly includes four parts: using a trajectory compression algorithm that takes into account the vessel motion characteristics to preprocess the vessel trajectory data; displaying and replaying vessel trajectories based on Electronic Chart System (ECS), and proposing a detection algorithm for vessel stay points based on the principle of spatiotemporal density to semantically label vessel trajectories; using the Fast Dynamic Time Warping (FastDTW) similarity measurement algorithm and the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to cluster vessel trajectories to show the differences and similarities between vessel traffic flows; and showing the distribution of vessels and the variation trend of vessel density based on the vessel heatmap. Based on the AIS data of the Yangtze River Estuary, three cases are used to prove the usefulness and effectiveness of the system in vessel behaviour analysis.

In inland waterways, there is a high density of various objects, with a predominance of small objects, which can easily affect navigation safety. To improve the navigation safety of inland ships, this paper proposes a new lightweight water-surface object-detection model named LWS-YOLOv7, which is based on the baseline model YOLOv7. Firstly, the localization loss function is improved and the w-CIoU function is introduced to reduce the model’s sensitivity to position deviations of small objects and to improve the allocation accuracy of positive and negative sample labels. Secondly, a new receptive field amplification module named GSPPCSPC is proposed to reduce the model’s parameters and enhance its receptive field. Thirdly, a small-object feature-fusion layer, P2, is added to improve the recall rate of small objects. Finally, based on the LAMP model pruning method, the weights with lower importance are pruned to simplify the parameters and computational complexity of the model, facilitating the deployment of the model on shipborne devices. The experimental results demonstrate that, compared to the original YOLOv7 model, the map of LWS-YOLOv7 increased by 3.1%, the parameters decreased by 38.8%, and the GFLOPS decreased by 28.8%. Moreover, the model not only has better performance and higher speed for input images of different sizes, but it can also be applied to different meteorological conditions.

Smoke simulation is a crucial yet challenging aspect of constructing ship fire scenarios. For the Eulerian smoke simulation method, the low-resolution grid results in a loss of smoke detail, while the high-resolution grid faces significant computational costs. To address this issue, a detail enhancement approach is proposed for smoke simulation in ship fire scenarios based on vortex particles, aiming at high-realism smoke simulation on a low-resolution grid. The simulation domain is first discretized using a low-resolution grid to compute the basic flow. Next, the vortex particles are sampled within the grid, and the loss of vorticity is measured before and after vortex stretching to compensate for the missing smoke details. In our approach, a geometric method is employed to efficiently capture the stretching of vortex structures. The computational results demonstrate that turbulence details can be effectively captured in a low-resolution grid while maintaining the real-time performance of the simulation. The practical application value of our approach is demonstrated in improving the realism of ship fire scenarios.