Search Results (74)

In the medical field, magnesium (Mg) alloys have been widely used due to their excellent antibacterial properties and biodegradability. However, in the marine environment, the antibacterial effect may be greatly attenuated, and consequently, microorganisms in the ocean are likely to adhere to the surface of Mg alloys, resulting in biocorrosion damage, which is really troublesome in the maritime industry and can even be disastrous to the navy. Currently, there is a lack of research on the biocorrosion of Mg alloys that may find important applications in marine engineering. In this paper, the biocorrosion mechanism of the Mg alloy Mg-3Nd-2Gd-Zn-Zr caused by Chlorella vulgaris (C. vulgaris), a typical marine microalga, was studied. The results showed that the biomineralization process in the artificial seawater containing a low concentration of C. vulgaris cells was accelerated compared with that in the abiotic artificial seawater, leading to the deposition of CaCO₃ on the surface to inhibit the localized corrosion of the Mg alloy, whereas a high concentration of C. vulgaris cells produced a high content of organic acids at some sites through photosynthesis to significantly accelerate the surface film rupture at some sites and severe localized corrosion there, but meanwhile, it resulted in the formation of a more protective biomineralized film in the other areas to greatly alleviate the corrosion. The contradictory biocorrosion behaviors on the Mg-3Nd-2Gd-Zn-Zr alloy induced by C. vulgaris were finally explained by a mechanism proposed in the paper. Full article

The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human resource management. This study proposes a resilience-based AX strategy and implementation system that allows domestic container-terminal companies to proactively respond to the upcoming changes in the global supply chain, thus securing sustainable competitiveness. In particular, we aim to design an AI-based governance model to establish a trust-based logistics supply chain (trust value chain). As a research method, the core risk factors of AX processes were scientifically identified via text-mining and fault-tree analysis, and a step-by-step execution strategy was established by applying a backcasting technique based on scenario planning. Additionally, by integrating social control theory with new governance theory, we designed a flexible, adaptable, and resilience-oriented AI governance system. The results of this study suggest that the AI paradigm shift should be promoted by enhancing the risk resilience, trust, and recovery of organizations. By suggesting AX strategies and policy as well as institutional improvement directions that embed resilience to secure the sustainable competitiveness of AI-based smart ports in Korea, this study serves as a basis for establishing strategies for the domestic container-terminal industry and for constructing a global leading model. Full article

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. Full article

Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime industries. Owing to their rapid spread and often unpredictable occurrence, timely and accurate detection is essential for effective containment and mitigation. An efficient detection system can significantly enhance the responsiveness of emergency teams, enabling targeted interventions that minimize ecological damage and economic loss. This paper proposes a marine radar-based oil spill detection method that combines the Significance-to-Boundary Ratio (SBR) feature with an improved Sauvola adaptive thresholding algorithm. The raw radar data was firstly preprocessed through mean and median filtering, grayscale correction, and contrast enhancement. SBR features were then employed to extract coarse oil spill regions, which were further refined using an improved Sauvola thresholding algorithm followed by a denoising step to obtain fine-grained segmentation. Comparative experiments using different threshold values demonstrate that the proposed method achieves superior segmentation performance by better preserving oil spill boundaries and reducing background noise. Overall, the approach provides a robust and efficient solution for marine oil spill detection and monitoring. Full article

The global shipping industry facilitates the movement of approximately 80% of goods across the world but accounts for nearly 3% of total greenhouse gas (GHG) emissions every year, and other pollutants. One challenge in reducing shipping emissions is understanding and quantifying emission characteristics. A detailed method for calculating shipping emissions should be applied when preparing exhaust gas inventory. This research focused on quantifying CO₂, NO_x, and SO_x emissions from tankers, containers, bulk carriers, and general cargo in the Republic of Korea using spatio-temporal analysis and maritime big data. Using the bottom-up approach, this study calculates vessel emissions from the ship engines while considering the fuel type and operation mode. It leveraged the Geographic Information System (GIS) to generate spatial distribution maps of vessel exhausts. The research revealed variability in emissions according to ship types, sizes, and operational modes. CO₂ emissions were dominant, totaling 10.5 million tons, NO_x 179,355.2 tons, and SO_x 32,505.1 tons. Tankers accounted for about 43.3%, containers 33.1%, bulk carriers 17.3%, and general cargo 6.3%. Further, emissions in hoteling and cruising were more significant than during maneuvering and reduced speed zones (RSZs). This study contributes to emission databases, providing a basis for the establishment of targeted emission control policies. Full article

As global awareness of sustainability and corporate social responsibility (CSR) intensifies, container shipping lines (CSLs) face growing pressure to align their operations with stakeholder expectations. However, existing studies in maritime logistics often examine CSR and environmental performance separately, rely on qualitative methods, or focus on broader shipping contexts without targeting CSLs specifically. Moreover, few studies provide data-driven benchmarking tools to evaluate performance across multiple sustainability dimensions. This study addresses these gaps by developing a quantitative benchmarking model that integrates entropy weighting and the grey relational analysis (GRA) to assess the performance of ten major CSLs using real-world data from 2022. The model incorporates operational, environmental, and social indicators, with entropy weighting objectively capturing the relative importance of each criterion. The GRA method is applied to rank CSLs based on their closeness to an ideal performer. A sensitivity analysis is then conducted by varying the distinguishing coefficient to test the robustness of the results. The findings reveal that cost-related criteria, such as the number of employees, energy consumption, and greenhouse gas emissions, carry the most weight. CSLs that perform consistently across multiple indicators tend to outperform peers that show inconsistency or rely heavily on a narrow set of strengths. This study contributes to the literature by offering an integrated, replicable approach for efficiency, sustainability, and CSR performance benchmarking in maritime logistics and by providing practical insights for policymakers, industry managers, and researchers. Full article

Marine biofouling is a well-established and significant challenge for the maritime industry. Self-healing coatings applied to ships have demonstrated superior surface properties, including enhanced corrosion resistance and the ability to mitigate biological contamination. Consequently, numerous studies have been conducted to assess different self-repairing coatings, which incorporate mechanisms such as microcapsules, dynamic covalent bonds, and ion exchange. This review begins with an introduction to the process of biofouling formation. It then provides a comprehensive outline of the self-healing coatings that have been developed to improve wear resistance, summarizing the advancements in this area. Finally, building upon these three coating systems, this paper offers a summary of the fabrication and protection technologies for self-healing coatings, including the preparation of micro/nano containers, corrosion warning mechanisms, and intelligent responsive protection. Furthermore, the review explores the future prospects of self-healing coatings, offering valuable insights for researchers in the field. The potential limitations of their application scenarios are also addressed. Full article

This study addresses the growing need for predictive maintenance in the maritime industry by proposing an optimized strategy for ship engine maintenance. The aim is to reduce unplanned failures that cause significant financial losses and disrupt global logistics flows. The methodology integrates Weibull reliability analysis, Markov chains, and Data Envelopment Analysis (DEA). A dataset of 512 diesel engine components from container ships was analysed, where the Weibull distribution (β = 1.8; α = 18,500 h) accurately modelled failure patterns, and Markov chains captured transitions between operational states (normal, degraded, failure). DEA was used to evaluate the efficiency of different maintenance strategies. Results indicate that targeting interventions in the degraded state significantly reduces downtime and improves component reliability, particularly for high-pressure fuel pumps and turbochargers. Optimizing maintenance extended the Mean Time to Failure (MTTF) up to 22,000 h and reduced the proportion of failures in critical components from 64.3% to 40%. These findings support a transition from reactive to proactive maintenance models, contributing to enhanced fleet availability, safety, and cost-effectiveness. The approach provides a quantitative foundation for predictive maintenance planning, with potential application in fleet management systems and smart ship platforms. Full article

The transition from the use of heavy fuel oil (HFO) to the use of green fuels (e.g., methanol) for container liner shipping presents a significant challenge for liner shipping system design (LSSD) in terms of achieving emission reductions. While methanol, including both green and gray methanol, offers environmental benefits, its lower energy density introduces operational complexities. Motivated by the aforementioned background, we establish a bi-level programming model. This model integrates liner speed management and bunker fuel management strategies (i.e., bunkering port selection and bunkering amount determination) with traditional network design decision (i.e., fleet deployment, shipping network design, and slot allocation) optimization. Specifically, the upper-level model optimizes the number of liners deployed in the fleet and shipping network structure, whereas the lower-level model coordinates decisions associated with liner sailing speed management, bunker fuel management, and slot allocation. Moreover, we propose an adaptive piecewise linearization approach combined with a genetic algorithm, which can efficiently solve large-scale instances. Sensitivity analyses of fuel types and fuel prices are conducted to demonstrate the effectiveness of the model and algorithm. Overall, our paper offers valuable insights for policymakers in designing customized emission reduction policies to support the green fuel transition in the maritime industry. Full article

(1) Background: The reliability of port equipment is of significant interest to industry stakeholders due to the economic and logistical factors governing the operation of maritime container terminals. Failures of key equipment like quay cranes can halt operations or cause economically significant delays. (2) Methods: The impact assessment of these disruptive events is conducted through terminal activity modeling and discrete-event simulation of internal processes. The system’s steady-state or transient condition, induced by disruptive events, is statistically assessed within a set of scenarios proposed by the authors. (3) Results: The Heidelberg–Welch and Geweke tests enabled the evaluation of steady-state and transient conditions within the modeled system, which was affected by the reduced reliability of container-handling equipment. (4) Conclusions: The research findings confirmed the usefulness of modeling and simulation in assessing the impact of equipment reliability on maritime container terminal operations. If the magnitude of the disruptive event exceeds the terminal’s absorption capacity, the system may become blocked or remain in a transient state without the ability to recover. This underscores the necessity of analyzing the reliability of critical handling equipment and implementing corrective maintenance actions when required. Full article

The imperative of decarbonization in maritime shipping is underscored by the sector’s sizeable contribution to worldwide greenhouse gas emissions. ShipNetSim, an open-source multi-ship simulator created in this study, combines state-of-the-art hydrodynamic modeling, dynamic ship-following techniques, real-time environmental data, and cybersecurity threat simulation to quantify and evaluate marine fuel consumption and CO₂ emissions. ShipNetSim uses well-validated approaches, such as the Holtrop resistance and B-Series propeller analysis with a ship-following model inspired by traffic flow theory, augmented with a novel module simulating cyber threats (e.g., GPS spoofing) to evaluate operational efficiency and resilience. In a case study simulation of the journey of an S175 container vessel from Savannah to Algeciras, the simulator estimated the total fuel consumption to be 478 tons of heavy fuel oil and approximately 1495 tons of CO₂ emissions for a trip of 7 days and 15 h within 13.1% of reported operational estimates. A twelve-month sensitivity analysis revealed a marginal 1.5% range of fuel consumption variation, demonstrating limiting variability for different environmental conditions. ShipNetSim not only yields realistic predictions of energy consumption and emissions but is also demonstrated to be a credible framework for the evaluation of operational scenarios—including speed adjustment, optimized routing, and alternative fuel strategies—that directly contribute to reducing the marine carbon footprint. This capability supports industry stakeholders and policymakers in achieving compliance with global decarbonization targets, such as those established by the International Maritime Organization (IMO). Full article

Maritime regulators are closely monitoring the progression of green shipping, and liner companies are seeking strategies to meet tough ship emission rules. To reduce the operating cost while conforming to the increasingly strict environmental regulations, the study first constructs a mixed-integer nonlinear optimization model. Subsequently, the nonlinear parts in the objective function and constraints are transformed into linear forms. Thereafter, the model is applied to the Asia–Europe route of the CMA CGM Shipping Company to find the planned speeds and bunkering strategies for container liners sailing in expanded emission control areas (ECAs) that will be implemented in the future. Finally, a sensitivity analysis is performed to examine the influence of bunker tank capacity and fuel price difference on the operating cost, carbon dioxide emission, bunkering strategy and planned sailing speed. The study contributes to determining the optimal tank capacity and developing bunkering strategies at different fuel price differences. With stricter policies, operators must strategically choose refueling ports, adjust refueling amounts, and optimize planned sailing speeds based on ship and route data. The proposed approach provides a solution to the contradiction between compliance with environmental regulations and cost-effectiveness of shipping companies and is of great significance for promoting the sustainable development of the waterway transportation industry. Full article

This paper provides a comprehensive review of truck appointment scheduling models and algorithms that support truck appointment systems (TASs) at container terminals. TASs have become essential tools for minimizing congestion, reducing wait times, and improving operational efficiency at the port and maritime industry. This review systematically categorizes and evaluates existing models and optimization algorithms, highlighting their strengths, limitations, and applicability in various operational contexts. We explore deterministic, stochastic, and hybrid models, as well as exact, heuristic, and metaheuristic algorithms. By synthesizing the latest advancements and identifying research gaps, this paper offers valuable insights for academics and practitioners aiming to enhance TAS efficiency and effectiveness. Future research directions and potential improvements in model formulation are also discussed. Full article

The International Maritime Organization (IMO) aims for net zero emissions in shipping by 2050. Ports, key links in the supply chain, are embracing green innovation, focusing on efficient berth and quay crane scheduling to support green port development amid limited resources. Additionally, the energy consumption and carbon emissions from the port shipping industry contribute significantly to environmental challenges and the sustainable development of ports. Therefore, reducing carbon emissions, particularly those generated during vessel berthing, has become a pressing task for the industry. The increasing complexity of berth allocation now requires compliance to vessel service standards while controlling carbon emissions. This study presents an integrated model that incorporates tidal factors into the joint optimization of berth and quay crane operations, addressing both service standards and emissions during port stays and crane activities, and further designs a PSO-GA hybrid algorithm, combining particle swarm optimization (PSO) with crossover and mutation operators from a genetic algorithm (GA), to enhance optimization accuracy and efficiency. Numerical experiments using actual data from a container terminal demonstrate the effectiveness and superiority of the PSO-GA algorithm compared to the traditional GA and PSO. The results show a reduction in total operational costs by 24.1% and carbon emissions by 15.3%, highlighting significant potential savings and environmental benefits for port operators. Furthermore, the findings reveal the critical role of tidal factors in improving berth and quay crane scheduling. The results provide decision-making support for the efficient operation and carbon emission control of green ports. Full article

Seaports are experiencing several challenges due to the explosive growth of the maritime shipping business, which has led to the need for digitalized operations and more effective solutions. This article provides a comprehensive exploration of the process used to create a reliable business intelligence solution by analyzing the container delivery and pick-up services flow in one of Portugal’s largest maritime container ports, using the CRISP-DM methodology. The solution, built with Microsoft Power BI^®, provides the capability to identify and address data anomalies and present key performance indicators in visually dynamic dashboards. This solution empowers stakeholders to gain invaluable insights into the current and future operational status, thereby facilitating well-informed and adaptable decision-making, representing the main practical contributions. As a theoretical contribution, this study advances research by covering a gap in the literature and establishing the foundations for future business intelligence applications within the maritime industry, with a focus on addressing data dispersion challenges, enhancing logistics flow analysis, and reducing port congestion. The manuscript is structured into seven sections: introduction, literature review, port challenges, methodology, tool development, SWOT analysis, and conclusion. Full article