As the CII regulations took effect in 2023, shipping companies are under increasing pressure to comply with these stringent regulations. This paper presents a fleet optimization method for CII regulatory constraints, considering the difference in main engine fuel consumption performance within the fleet. Two scenarios are optimized: "Fleet scheduling considering single ship CII rating limits" and "Fleet scheduling with no E-class ships and the least used biofuel". The results demonstrate that the proposed optimization algorithm outperforms random scheduling in maintaining fleet CII compliance and reducing biofuel usage. This study will help shipping companies meet fleet CII regulations and reduce carbon emissions, while improving their ability to respond to international green and low-carbon regulations.

China rivers are abundant in water resources, and inland container transport is the main form of river transport. However, with the rapid development of road construction, the continuous improvement of the comprehensive transport system and increased competition in road transportation resulting in lower freight costs, inland container transport will also face huge challenges. In order to utilise the economical and energy-saving role of inland river transport more effectively, it is necessary to encourage owners of goods to choose the waterway transportation mode. From the perspective of the inland container transport system, this paper conducts a quantitative analysis to address the issue of modal choice between inland container transport and road transport, internalising the economic externality of carbon emission through the total cost of transport. Taking into account the low-carbon objective, a transportation mode choice equilibrium model based on an inland container transport system was constructed. Meanwhile, the paper focuses on analysing the economic characteristics of total transportation costs and related influencing factors using an economic approach. Finally, the model's validity is verified using numerical examples. The results demonstrate that, under the low-carbon objective, it is more economical for owners to choose waterway transportation when the transportation distance is more than 1 100 km.

To address the issues of the analytical-based F_QSD (Fuzzy Quaternion Ship Domain) model, such as the difficulty in defining its boundary, insufficient consideration of influencing factors, and challenges in practical application, this paper incorporates the human factor of ship navigator and establishes a D_QSD (Dynamic Quaternion Ship Domain) based on the ship navigator's state to enhance the analytical-based fuzzy quaternion ship domain. The shape parameter K value of the fuzzy quaternion ship domain is calculated according to the ship navigator's state, and the dynamic ship domain model under different ship navigator states is obtained through computer simulation. The results show that the shape and area of the ship domain dynamically change under different driving states. When the ship navigator's state is excellent, the ship domain shape is an irregular rhombus, and the domain area is the smallest. When the ship navigator's state is poor, the ship domain shape is approximately rectangular, and the domain area is the largest. Compared with the traditional analysis-based ship domain, the proposed model determines the fuzzy boundary of the ship domain according to the ship navigator's state, making the dynamic ship domain more flexible and adaptable.

Complex meteorological sea conditions directly affect the safety of ship navigation, and the accuracy of the prediction of offshore wind speed, as a major factor in meteorological sea conditions, is of great significance to the navigation safety and trajectory planning. In order to effectively improve the accuracy of offshore wind speed prediction and overcome the limitations of a single prediction model, the offshore wind form data of Lianyungang station is used as an example study, and the Adaboost algorithm is used to integrate the advantages of multi-models to construct a combined prediction model of offshore wind speed. Four time series prediction models, including BP neural network, GA BPNN, long and short-term memory network and WOA-SVR, are used for wind speed prediction. Considering the prediction effect of a single model, Adaboost algorithm is applied to integrate the GA-BPNN model and WOA-SVR model to construct the combined offshore wind speed prediction model, and the integration accuracy is compared with that of Bagging algorithm. The results show that the root mean square error of the combined prediction model with the Adaboost algorithm is reduced by about 13% and the mean absolute error is reduced by about 16% compared with the single model, which effectively verifies the superiority of the combined prediction model in the prediction of offshore wind speed data, and it is of great significance for the enhancement of navigational safety and the optimization of the trajectory design.

All-electric tugboats (AETs) produce fewer carbon emissions, but their battery charging requirements can lead to ship delays, which may increase the overall carbon emission costs in ports. Therefore, it is essential to study the carbon emission reduction performance of AETs in ports and compare their effectiveness with that of diesel tugboats (DTs). This paper establishes scheduling optimization models for AETs and DTs, respectively, using the sum of tugboat carbon emissions and ship delay carbon emissions as the objective function. Taking a day's data from three port areas in Ningbo-Zhoushan Port as an example, the Gurobi solver is employed to find the optimal solution. A comparative study is conducted on the minimum tugboat carbon emission cost and total carbon emission cost when AETs and DTs with the same horsepower and quantity are used to handle the same ships. The results show that if only the carbon emission cost of tugboats is considered, AETs can reduce carbon emission costs by 9.5% to 22.9% compared to DTs. However, when considering the overall carbon emission cost of the port areas, AETs still demonstrate good carbon emission reduction effects when handling fewer than 8 ships. When the number of ships exceeds 8 and the port is busy, the charging requirements of AETs increase the overall carbon emission cost of the port by 24.68%, which is not conducive to port carbon emission reduction.

Based on the typical container terminal loading and unloading process, starting from the transport ships, loading and unloading equipments, yard layout parameters, loading and unloading work characteristics, standardized experimental test methods and experimental parameters of energy consumption of loading and unloading machinery in container terminal are formulated, and based on the formulated experimental methods, energy consumption test experiments of loading and unloading machinery in container terminal are carried out, and the sample data of energy consumption of loading and unloading machinery in container terminal are obtained. The kurtosis and skewness test method was applied to analyze and verify the distribution law of the energy consumption test data, and the energy consumption limit value of container terminal loading and unloading equipments was finally obtained based on the statistical quota of the energy consumption test data and the corresponding national standards, combined with the relevant theory of probability theory.

The traditional A* algorithm applied to the path planning of offshore wind farm operation and maintenance ships has not yet taken into account the dynamic obstacles, water currents, and crossing navigation channels, therefore, this paper proposes a path planning method that considers navigational risks, heading angle constraints, and path smoothing. On the basis of constructing the map of offshore wind farm water environment by raster method, weight coefficients are introduced to change the proportion of estimated surrogate value in the total cost function of the A* algorithm to achieve the purpose of balancing the strength of heuristic information and shortening the pathfinding time, and the risk of obstacles containing water currents is taken into account in order to improve the actual cost function of the A* algorithm and enhance the security of the planned paths. Meanwhile, the heading angle constraint is considered in the A* algorithm to reduce the total number of traversal nodes, the eight-neighborhood search is constrained to three neighboring nodes conforming to the path direction, the inflection points are extracted and visibility check is performed to remove the redundant inflection points in the path, and the smooth planning path is obtained using a uniform B-spline curve. Taking the Donghai Bridge No.5 and No.6 wind farm waters as an example, a high tide path planning scenario is established, and the operation and maintenance ship needs to pass through 9 wind turbines in order to complete the operation and maintenance tasks; 4 indexes (path length, total risk value of the path, total number of traversed nodes, and total number of inflection points) are utilized for evaluating the planning path, so as to validate the effectiveness of the improved A* algorithm. The simulation results show that in the high tide scenario, the planning path smoothness of the improved A* algorithm is improved by 77.69%, the total risk value of the planning path is reduced by 52.83%, and the total number of traversal nodes is reduced by 30.58%, but the planning path length of the improved A* algorithm is 252.89 m longer than that of the traditional A* algorithm.

A Vector Auto Regression (VAR) model is constructed by combining the China Containerized Freight Index of E/C America Service (CCFI E/C America Service) and the China Containerized Freight Index of W/C America Service (CCFI W/C America Service) with the Clarksons Container ship Port Congestion Index (CPCI) from January 2018 to February 2023, to quantitatively analyze the impact mechanism of port congestion on container freight rates. The model also incorporates a Vector Error Correction (VEC) model to study the long-run equilibrium relationship between the variables. The results show that: 1) Port congestion leads to the occupation of container capacity and port resources, as well as changes in the distribution of capacity and transportation strategies on the China-U.S. export container routes, which in turn causes different degrees of fluctuations in CCFI on the sub-routes; 2) The effect of port congestion on container freight rates persists for nearly three months; 3) Regardless of the U.S. East route or the U.S. West route, port congestion in the U.S. has a more significant impact on promoting the increase of the container freight index compared to port congestion in China. Meanwhile, this paper provides a new perspective for predicting CCFI by investigating the impact mechanism of port congestion on CCFI fluctuations.

Study on the location selection problem of multi-level offshore ship oil spill emergency equipment depots is of great guiding significance for the effective utilization of oil spill emergency resources, the improvement of regional oil spill emergency response capabilities, and the perfection of the oil spill emergency response system. According to the distribution of oil spill risk points and the predicted oil spill volume, combined with the characteristics of the emergency service radius and comprehensive removal and control capabilities of oil spill emergency equipment depots at different levels, a location selection model for multi-level offshore ship oil spill emergency equipment depots is established with the objectives of achieving the best coverage, the highest reliability, and the strongest timeliness for different risk waters. Then, the MOPSO algorithm and NSGA- Ⅱ algorithm are used to solve the model, and the Pareto optimal solution set is obtained. Starting from the perspective of coordinated rescue and based on the emergency service capabilities of equipment depots at different levels, the location selection model realizes the best multiple coverage of different risk waters, aiming to share emergency resources and further enhance the regional emergency response capabilities.

In order to improve the safety of water transportation and reduce collision accidents, a quantitative model of ship collision hazard based on complex network is proposed. By constructing the complex network and calculating the relationship strength to reflect the relationship between ships, the RDF (Radial Distribution Function) is used to analyze the density factor of the ship and discuss the traffic situation around the ship; the centrality of the network is used to analyze the conflict factor of the ship; and the affiliation function based on the power law of Stevens is used to integrate the density factor and the conflict factor to improve the collision hazard quantification method based on ship pairs. In order to prove the accuracy of the model, example verification is carried out and compared with the method based on the superposition calculation of ship pairs, and the results show that the present model has a certain degree of accuracy. By analyzing the actual water situation, the corresponding warning mechanism is discussed, which can be used for monitoring the water traffic situation and hazard warning.