Search Results (179)

The sustainability of a port is directly related to the time spent by ships in terminals and depends on the terminal, the technologies used in it, and external conditions. Currently used sustainable port terminal technologies allow a significant increase in the intensity of ship loading operations and, at the same time, shorten the time spent by ships at the quays. Since port construction processes take a lot of time, many ports have many quays every day that are not moored by ships. Ports try to attract passenger and cargo flows, but they are also not infinite. In individual port terminals, for example, container and Ro–Ro terminals, most of the time is spent on cargo processing inside the terminal, and only part of the time is spent on ship loading operations. Probabilistic assessment of ship mooring at quays allows an understanding of not only the optimal need for quays and modernization of their equipment, but at the same time for a more purposeful assessment of the possibilities of using quays, accepting diversification options and, therefore, optimizing the ports themselves as a sustainable port entity. The article presents a methodology for assessing berth occupancy focused on the development of a sustainable port based on probabilistic methods that would allow calculating potential berth occupancy. The developed methodology, compared to existing methodologies and models, allows for a more realistic assessment of the expected berth occupancy, using actual port and ship data. The presented theoretical and experimental research results confirm the suitability of the developed methodology for the development of a sustainable port and the possibilities of applying the developed methodology in any port, adapting it to specific port conditions. Full article

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy. Full article

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO₂, 20,500 tons of NO_x, 4250 tons of SO₂, 656 tons of PM₁₀, and 603 tons of PM_2.5, with anchorage activities contributing 497,000 tons of CO₂, 7010 tons of NO_x, 1520 tons of SO₂, 232 tons of PM₁₀, and 214 tons of PM_2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies. Full article

Every year, more and more general and other types of cargo are transported by containers, and many ports, including small and medium-sized ones, are trying to join the container transportation processes. Port connectivity with container shipping is associated with easier and faster cargo processing and reduced environmental impact by optimizing ship arrivals and processing in small and medium-sized ports. Small and medium-sized ports are often limited by port infrastructure, especially suitable quays; therefore, it is very important to correctly assess the capabilities of such ports so that ships do not have to wait for entry and so that quays and other port infrastructure are optimally used. The research is relevant because small and medium-sized ports are increasingly involved in the activities of logistics chains and are becoming very important for the development of individual regions. The wider use of small and medium-sized ports in logistics chains is a new and original research direction. Optimal assessment of port or terminal and berth utilization is possible using the principles of probability theory. The article develops and presents a probabilistic method for assessment of port and terminal and ship mooring at their berths, using possible and actual time periods, based on the principles of transport process organization and linked to the capabilities of the port infrastructure and terminal superstructure. The conditional probability method was used to assess port and terminal capacity, as well as a method for assessing ship maneuverability under limited conditions. The developed probabilistic method for assessing port terminals and ship berthing at port quays can be used in any port or terminal, taking into account local conditions. Combined theoretical research and experimental results of the optimal use of small and medium-sized ports ensure sufficient research quality. Full article

This study investigates the configuration of an energy storage system (ESS) and the optimization of energy management strategies for diesel-electric hybrid ships, with the goal of enhancing fuel economy and reducing emissions. An integrated mathematical model of the diesel generator set and the battery-based ESS is established. A rule-based energy management strategy (EMS) is proposed, in which the ship operating conditions are classified into berthing, maneuvering, and cruising modes. This classification enables coordinated power allocation between the diesel generator set and the ESS, while ensuring that the diesel engine operates within its high-efficiency region. The optimization framework considers the number of battery modules in series and the upper and lower bounds of the state of charge (SOC) as design variables. The dual objectives are set as lifecycle cost (LCC) and greenhouse gas (GHG) emissions, optimized using the Multi-Objective Coati Optimization Algorithm (MOCOA). The algorithm achieves a balance between global exploration and local exploitation. Numerical simulations indicate that, under the LCC-optimal solution, fuel consumption and GHG emissions are reduced by 16.12% and 13.18%, respectively, while under the GHG-minimization solution, reductions of 37.84% in fuel consumption and 35.02% in emissions are achieved. Compared with conventional algorithms, including Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Dung Beetle Optimizer (NSDBO), and Multi-Objective Sparrow Search Algorithm (MOSSA), MOCOA exhibits superior convergence and solution diversity. The findings provide valuable engineering insights into the optimal configuration of ESS and EMS for hybrid ships, thereby contributing to the advancement of green shipping. Full article

Maritime transportation companies operate in highly volatile environments, where data-driven decision-making is critical to navigating fluctuating freight revenue, fuel and transit costs, and dynamic trade-related policies. This study addresses the liner service network design and container flow management problem, with the objective of maximizing weekly profit, calculated as total freight revenue minus comprehensive operational costs associated with fuel, berthing, transit, and policy-driven extra fees. We formulate a mixed-integer programming (MIP) model for the problem and demonstrate that the constraint matrix associated with vessel leasing is totally unimodular. This property permits the reformulation of the original MIP model into a semi-relaxed MIP model, which maintains optimality while improving computational efficiency. Using shipping data in a realistic liner service network, the proposed model demonstrates its practical applicability in balancing complex trade-offs to optimize profitability. Sensitivity analyses provide actionable insights for data-driven decision-making, including when to expand service networks, discontinue unprofitable routes, and strategically deploy vessel leasing to mitigate rising operational costs and regulatory penalties. This study provides a practical, computationally efficient, and data-driven framework to support liner shipping companies in making robust tactical decisions amid economic and regulatory dynamics. Full article

This work addresses the automatic berthing control problem of underactuated ships under false data injection (FDI) attack, and an event-sampled automatic berthing control scheme is proposed. To avoid the FDI attack signals from entering the closed-loop system through the sensor–controller channel and worsening the berthing control performance as much as possible, a novel event-sampled adaptive neural network state observer is developed, which is independent of the controller. To solve the control design problem of berthing caused by underactuated features, an equivalent motion model of underactuated ships under FDI attack is established by differential homeomorphic transformation. Furthermore, under the backstepping design framework, using the state observer and adaptive neural network technology, a single-parameter learning-based automatic berthing control solution is developed. Meanwhile, to further reduce the network resource consumption and load caused by the transmission of control signals, an event-triggered mechanism for the controller–actuator channel is established. The theoretical analysis by Lyapunov indicates that the constructed closed-loop system for automatic berthing control is stable, and all the signals are bounded. Simulation and comparison are carried out to verify the effectiveness and superiority of proposed control scheme, and the results verify the conclusions and theoretical feasibility of this work. Full article

This study investigates the impact of reducing waiting times for port berth on improving the Carbon Intensity Indicator (CII) ratings of Korean-flagged container ships. As the International Maritime Organization (IMO)’s CII regulation mandates corrective actions for poorly rated ships for Greenhouse Gas (GHG) reduction in international shipping, the analysis focuses on container ships with projected D or E ratings by 2035. Using Automatic Identification System (AIS) data from ships, this study identifies annual waiting times and simulates changes in CII ratings under scenarios of reduced waiting times (30%, 50%, 70%, and 100%). The relationship between ship speed and fuel consumption was established by analyzing the recent literature, and the CII improvement was evaluated based on IMO Data Collection System (DCS) 2022 data. The results show that a 30% reduction in waiting time can lower CO₂ emissions by 12.18% and improve the CII rating by one or two levels for approximately half of the sample ships. However, a 50% reduction or more is required to maintain improved ratings beyond 2030. The findings highlight the significance of just-in-time (JIT) practices in minimizing latency and enhancing regulatory compliance. The policy recommendations advocate for prioritizing port call optimization and recommend the adoption of JIT as a measure to achieve the IMO’s GHG reduction targets. Full article

This study conducts a high-resolution forensic evaluation of stern structural damage resulting from a tugboat collision during berthing, integrating real-world measurement data with calibrated nonlinear finite element analysis. Based on field-acquired deformation geometry and residual dent profiles at Frame 76, five distinct collision scenarios varying in impact orientation, contact area, and load path were simulated using shell-based nonlinear plastic analysis. Particular attention is given to comparing the plastic equivalent strain (PEEQ), von-Mises stress fields, and residual deformation contours at Point A—the critical zone identified from damage surveys. Among the five cases, Case-2, defined by a vertically eccentric external impact, demonstrated the highest plastic strain intensity (PEEQ > 2.0%), the sharpest post-yield drops in stiffness, and the closest match to the residual dent profile observed in the actual structure. The integrated correlation between field damage and some of the results (strain, stress, and deformed shape) enabled clear identification of the most probable accident mechanism with engineering accuracy. This study proposes a validated, measurement-calibrated nonlinear finite element analysis framework to diagnose stern damage from tugboat collisions, enhancing repair decision-making and structural safety assessment. Such a calibrated forensic strategy enhances the reliability of structural safety predictions in marine collision incidents and supports eco-friendly rescue engineering by minimizing unnecessary structural renewal through precise damage localization. The proposed approach establishes a new benchmark for scenario-driven collision assessment, particularly relevant to sustainable, automation-compatible, and damage-tolerant ship design practices. Full article

Accurate ship berthing behavior prediction (BBP) is essential for enabling collision warnings and support decision-making. Existing methods based on Automatic Identification System (AIS) data perform well in the task of ship trajectory prediction over long time-series and large scales, but struggle with addressing the fine-grained and highly dynamic changes in berthing scenarios. Therefore, the accuracy of BBP remains a crucial challenge. In this paper, a novel BBP method based on Light Detection and Ranging (LiDAR) data is proposed. To test its feasibility, a comprehensive dataset is established by conducting on-site collection of berthing data at Dalian Port (China) using a shore-based LiDAR system. This dataset comprises equal-interval data from 77 berthing activities involving three large ships. In order to find a straightforward architecture to provide good performance on our dataset, a cascading network model combining convolutional neural network (CNN), a bi-directional gated recurrent unit (BiGRU) and bi-directional long short-term memory (BiLSTM) are developed to serve as the baseline. Experimental results demonstrate that the baseline outperformed other commonly used prediction models and their combinations in terms of prediction accuracy. In summary, our research findings help overcome the limitations of AIS data in berthing scenarios and provide a foundation for predicting complete berthing status, therefore offering practical insights for safer, more efficient, and automated management in smart port systems. Full article

With the continuous expansion of global trade, achieving sustainable maritime transport optimization and operations has become a key strategic direction for transforming maritime transport companies. To summarize the current state of research and identify emerging trends in sustainable maritime transport optimization and operations, this study systematically examines representative studies from the past decade, focusing on three dimensions, technology, management, and policy, using data sourced from the Web of Science (WOS) database. Building on this analysis, potential avenues for future research are suggested. Research indicates that the technological field centers on the integrated application of alternative fuels, improvements in energy efficiency, and low-carbon technologies in the shipping and port sectors. At the management level, green investment decisions, speed optimization, and berth scheduling are emphasized as core strategies for enhancing corporate sustainable performance. From a policy perspective, attention is placed on the synergistic effects between market-based measures (MBMs) and governmental incentive policies. Existing studies primarily rely on multi-objective optimization models to achieve a balance between emission reductions and economic benefits. Technological innovation is considered a key pathway to decarbonization, while support from governments and organizations is recognized as crucial for ensuring sustainable development. Future research trends involve leveraging blockchain, big data, and artificial intelligence to optimize and streamline sustainable maritime transport operations, as well as establishing a collaborative governance framework guided by environmental objectives. This study contributes to refining the existing theoretical framework and offers several promising research directions for both academia and industry practitioners. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

To address the issues of path tracking accuracy and control stability in autonomous ship berthing, an improved algorithm combining nonlinear model predictive control (NMPC) and convolutional neural networks (CNNs) is proposed in this paper. A CNN is employed to train on a large dataset of ship berthing trajectories, combined with the rolling optimization mechanism of NMPC. A high-precision path tracking control method is designed, which accounts for ship motion constraints and environmental disturbances. Simulation results show an 88.24% improvement in tracking precision over traditional MPC. This paper proposes an improved nonlinear model predictive control (NMPC) strategy for autonomous ship berthing. By integrating convolutional neural networks (CNNs) and moving horizon estimation (MHE), the method enhances robustness and path-tracking accuracy under environmental disturbances. The amount of system overshoot is reduced, and the anti-interference capability is notably improved. The effectiveness, generalization, and applicability of the proposed algorithm are verified. Full article

Indonesia’s high logistics costs—approximately 14.6% of its GDP—pose a significant challenge to national economic competitiveness. Key contributing factors include complex geography, fragmented multimodal transport systems and inefficient container terminal operations, particularly concerning the handling of empty containers. This study investigates operational optimization in a container terminal using Agile and Lean principles, without additional investment or infrastructure expansion. It compares throughput before and after optimization, focusing on equipment productivity and reduction in idle time, especially related to equipment and human resources. Field implementation began in 2015, followed by simulation-based validation using system dynamics modeling. The terminal demonstrated a sustained increase in capacity beginning in 2016, eventually exceeding its original design capacity while maintaining acceptable berth and Yard Occupancy Ratios (BOR and YOR). Agile practices improved empty container handling, while Lean methods enhanced berthing process efficiency. The findings confirm that significant reductions in port operational costs, shipping operational costs, voyage turnover time, and logistics costs can be achieved through strategic operational reforms and better resource utilization, rather than through capital-intensive expansion. The study provides a replicable model for improving terminal efficiency in ports facing similar constraints. Full article

Autonomous ship berthing requires advanced path planning to balance multiple objectives, such as minimizing berthing time, reducing energy consumption, and ensuring safety under dynamic environmental constraints. However, traditional planning and learning methods often suffer from inefficient search or sparse rewards in such constrained and high-dimensional settings. This study introduces a double deep Q-network (DDQN)-guided dual-population constrained multi-objective evolutionary algorithm (CMOEA) framework for autonomous ship berthing. By integrating deep reinforcement learning (DRL) with CMOEA, the framework employs DDQN to dynamically guide operator selection, enhancing search efficiency and solution diversity. The designed reward function optimizes thrust, time, and heading accuracy while accounting for vessel kinematics, water currents, and obstacles. Simulations on the CSAD vessel model demonstrate that this framework outperforms baseline algorithms such as evolutionary multitasking constrained multi-objective optimization (EMCMO), DQN, Q-learning, and non-dominated sorting genetic algorithm II (NSGA-II), achieving superior efficiency and stability while maintaining the required berthing angle. The framework also exhibits strong adaptability across varying environmental conditions, making it a promising solution for autonomous ship berthing in port environments. Full article