Search Results (167)

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping. 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

The present study addresses the topic of European companies, including Latvian companies, sustainably entering African markets. The actuality of this topic relates to the recession and the decrease in demand in the classical export markets (such as Scandinavia and Western Europe) with which Latvian firms used to trade; this is why the re-orientation of companies to African countries was carried out. Academic research worldwide has conducted many investigations on the specifics of exporting to Africa. The lack of knowledge relating to local African business practices is considered one of the significant barriers. The aim of this study was to mitigate this barrier by exploring real-world situations in African economic sectors. Interviews with relevant African experts were conducted for this purpose. The results showed that East European entrepreneurs, including Latvian entrepreneurs, should first focus on West African French-speaking countries with big seaports (e.g., Senegal, Guinea, Ivory Coast, Benin, Togo, and Cameroon), where Latvian knowledge, professional skills, and products relating to port and transportation infrastructures are in significant demand. A case study was conducted in Cameroon as an example of a good business match with Latvian service providers. The case study also highlighted the nature of Cameroon’s sociocultural dynamics, which are distinguished by the presence of several sociocultural zones, each with its own specific characteristics that need to be taken into account. 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

This paper proposes a combined optimization method for multi-period empty container repositioning and inventory control based on adaptive particle swarm optimization (APSO) algorithm, which addresses the limitations of existing research, such as decoupling empty container repositioning and inventory control optimization, and lacking multi-period dynamic collaboration mechanisms. Firstly, a joint optimization model integrating (s, S) inventory control strategy is constructed. By adopting the strategy, the selection of repositioning paths and inventory resource allocation are synergistically optimized to balance unit empty container rental costs, inventory costs, and repositioning costs. Secondly, we design an adaptive particle swarm optimization algorithm, introduce dynamic inertia weight and acceleration coefficient adjustment mechanisms, and design heuristic rules for empty container repositioning. In this way, we reduce unreasonable empty container mobilization through the setting of surplus, shortage, and balance ports of empty containers, which can narrow the search space and improve the algorithm’s global search ability and convergence efficiency in high-dimensional decision spaces. Numerical experiments show that the joint optimization model designed can reduce the total cost of empty container management for shipping companies and maintain the rental cost in a stable state. Sensitivity analysis reveals that the unit container rental cost and the maximum inventory capacity of the port have a significant impact on the total system cost, providing a new approach for shipping companies to reduce empty container management costs. Full article

Port shipping collaboration is vital to greener, more resilient trade, yet decisions remain siloed and uncertain. This study develops a Bayesian network model grounded in empirical data from major Chinese ports, aiming to systematically analyze and enhance port shipping collaborative capacity. The methodology integrates expert knowledge and structural learning algorithms to construct a Directed Acyclic Graph (DAG), representing complex multi-stakeholder interactions among port enterprises, shipping companies, customers, and governmental bodies. Through forward and backward probabilistic inference, the study quantifies how coordinated improvements yield substantial synergistic benefits. Five leverage points stand out: customer engagement in green supply chains, perceived service quality, port digital information integration, multilateral trading maturity, and strict policy enforcement. A newly revealed feedback loop between digital integration and enforcement extends Emerson et al.’s collaborative governance framework, highlighting “digital-era connectivity” as a critical governance dimension and offering managers a focused, evidence-based action agenda. Full article

The shipping industry, a cornerstone of global trade, faces emissions reduction challenges amid tightening environmental policies. Blockchain technology, leveraging distributed symmetric architectures, enhances supply chain transparency by reducing information asymmetry, yet its dynamic interplay with emissions strategies remains underexplored. This study employs symmetry-driven differential game theory to model four blockchain scenarios in port-shipping networks: no blockchain (N), port-led (PB), shipping company-led (CB), and a joint platform (FB). By solving Hamilton–Jacobi–Bellman equations, we derive optimal emissions reduction efforts, green investments, and blockchain strategies under symmetric and asymmetric decision-making frameworks. Results show blockchain adoption improves emissions reduction and service quality under cost thresholds, with port-led systems maximizing low-cost profits and shipping firms gaining asymmetrically in high-freight contexts. Joint platforms achieve symmetry in profit distribution through fee-trust synergy, enabling win–win outcomes. Integrating graph-theoretic principles, we have designed dynamic state equations for emissions and service levels, segmenting shippers by low-carbon preferences. This work bridges dynamic emissions strategies with blockchain’s network symmetry, fostering economic–environmental synergies to advance sustainable maritime supply chains. Full article

This paper presents a review of current developments in port pollution prevention and mitigation. A systematic categorization of the sources of pollution in the development and operation phases of ports and terminals is first presented. The paper then considers in detail technological and regulatory measures currently being applied to limit port pollution in the operation phase. This review is combined with that of relevant academic research and aims to fill a research gap by identifying the current and emerging port pollution themes and the latest trends in measures for pollution prevention and mitigation. A comprehensive approach is taken in this review by including not only academic research but also the industry’s research and development initiatives and the regulatory authority’s legislation. This paper identifies more than thirty different technological, regulatory, or organizational measures to limit pollution, although details on company-based research and development were found to be scarce. Mitigation of greenhouse gases and air-polluting emissions is identified as the most important field of research, but it is affected by regulatory uncertainties. Further research is needed on topics such as increased alternative fuel provision, digitalization potential for sustainability enhancement, and strategies for engaging stakeholders in greening ports. Full article

The ambition of ports to become green and smart ports is one of the important ways to reduce environmental impacts and optimize energy consumption in passenger service and cargo handling operations in ports. One of the ways to transform a green port is to use renewable energy sources, more environmentally friendly fuels and reduce emissions in passenger service and cargo handling operations. The article analyses the main factors of green port transformation and factors assessment, including port strategy, port management, passenger service and cargo handling operations (port activity level), additional port services, and the activities of companies providing services to the port. Optimization of the indicated factors is important from the point of view of environmental sustainability. The article presents a methodology for direct and relative assessment of the current state of the green transformation and emissions generated in the port and options for reducing the environmental impact. This approach enables each port to evaluate its stage in the green transformation process and identify the primary emissions it produces. By understanding the actual state of green transformation, ports can identify the factors and measures necessary to improve their environmental performance and reduce their ecological footprint. The article presents a methodology for assessing green transformation and calculating both absolute and relative emissions, which can be adapted and applied to any port. Full article

This study presents how integrating natural language processing (NLP) and machine learning (ML) optimizes strategic management in the port sector. Using hybrid NLP-ML models, the accuracy of classification and prediction of strategic information is significantly improved by analyzing large sets of textual data, both unstructured and semi-structured. The methodological approach is developed in three phases: first, a strategic analysis of port systems is performed using NLP; then, ML is integrated with NLP for text classification using advanced tools such as BERT and Word2Vec; finally, advanced models, including Decision Trees and Recurrent Neural Networks are evaluated. Applied to 55 companies in three countries, this method extracts key strategic data such as mission, vision, values and corporate objectives from their websites to obtain strategic terms related to innovation and sustainability. The study improves the ability to interpret textual data, enabling more informed and agile decision-making, which is essential in a highly competitive and dynamic environment. Full article

This paper presents a pilot project conducted by ISPRA and ARPA Campania to develop a monitoring protocol to detect the presence of genetically modified (GM) oilseed rape (Brassica napus) plants resulting from accidental seed dispersal during transportation from entry points to storage and processing facilities; the project has been implemented in Italy’s Campania region. The unintentional dispersal of GM oilseed rape seeds and the potential establishment of feral populations have been identified as environmental concerns in various countries, even when GM oilseed rape is imported solely for processing and not for cultivation. The project activities were designed, taking into account the characteristics of the Italian environment and infrastructures. Multiple sampling campaigns were conducted in autumn 2018, spring 2019, and autumn 2019 to validate the selected transects and assess the presence of Brassicaceae species, with a particular focus on oilseed rape. These efforts involved direct monitoring and sample collection along transport routes from the port of Salerno to seed companies in the provinces of Benevento and Caserta. Field observations and import data revealed a decrease in oilseed rape movement at the port of Salerno in the years preceding the survey, while seed companies near Benevento remained active sites for white mustard (Sinapis alba). The presence of S. alba and the simultaneous occurrence of oilseed rape and Raphanus raphanistrum—a species with high hybridization potential—support the hypothesis that seed companies may act as hotspots for accidental seed dispersal and that potential interspecific gene flow can occur. The study also validated the adopted sampling and molecular analysis methods, including DNA extraction and PCR, for the detection of the Cruciferin A (CruA) gene in all Brassica species collected. These findings highlight the need to strengthen post-marketing monitoring plans, even when GM rapeseed is imported solely for processing, to mitigate the potential risks associated with unintended gene flow. Full article

The ports and shipping industry is crucial in the global supply chain. Amid complex market and geopolitical dynamics, strengthening stakeholder collaboration becomes imperative to enhance maritime supply chain profit. Therefore, we develop a three-stage game model consisting of a port operator and a shipping company. We consider the impact of upgrading port facilities with advanced technology on the logistic decisions of the shipping company. In the first stage, the port decides whether to invest in upgrades, while the shipping company chooses one-way or two-way logistics. In subsequent stages, the port sets cargo handling charges, and the shipping company determines the freight rate. Equilibria under decentralized and centralized decision frameworks are derived. The equilibrium results show that market size has a significant effect on the shipping company’s choice. Specifically, the shipping company prefers two-way logistics when the market size is moderate, while one-way logistics is preferred when the market size is large or small. In addition, based on the fixed costs associated with port facility upgrades and two-way logistics, it is found that there exist three possible equilibria. Moreover, further analysis suggests that collaboration between the two parties, under appropriate financial conditions, can result in mutually beneficial outcomes. Our findings highlight the critical role of port–shipping company collaboration in enhancing operational efficiency and achieving greater mutual benefits. Full article

This study examines the impact of monitoring, reporting, and verification (MRV) system indicators on the costs associated with the emissions trading system (ETS) of the maritime sector in the European Union. Since maritime transport has recently been incorporated into the ETS, it becomes essential to understand how different operational and environmental factors affect the economic burden of shipping companies and port competitiveness. To this end, a model based on Bayesian networks is used to analyse the interdependencies between key variables, facilitating the identification of the most influential factors in the determination of the costs of the ETS. The results show that fuel efficiency and CO₂ emissions in port are decisive in the configuration of costs. In particular, it was identified that emissions during the stay in port have a greater weight than expected, which suggests that strategies such as the use of electrical connections in port (cold ironing) may be key to mitigating costs. Likewise, navigation patterns and traffic regionalisation show a strong correlation with ETS exposure, which could lead to adjustments in maritime routes. This probabilistic model offers a valuable tool for strategic decision-making in the maritime sector, benefiting shipping companies, port operators, and policymakers. However, future research could integrate new technologies and regulatory scenarios to improve the accuracy of the analysis and anticipate changes in the ETS cost structure. Full article

As the global market for shipping perishable goods expands, the substantial loss and high claim costs associated with these goods have drawn increasing attention. Blockchain technology (BCT) can improve customs clearance efficiency and reduce perishable goods loss. However, the high investment costs present a clear trade-off between enhancing clearance efficiency to mitigate loss and claims costs and the financial burden of BCT adoption. Additionally, determining which stakeholder should invest in BCT has become a critical strategic issue. To address this, we develop three Stackelberg game models to investigate the optimal BCT investment strategies for different entities—the port and the shipping company—in the maritime supply chain. Building on previous models in the existing literature, we incorporate the perishable goods loss rate and claim costs to offer new insights into how the perishable goods loss rate influences BCT investment outcomes. The results reveal that when the shipping company invests in BCT, if its BCT investment cost coefficient is within a certain range, a higher perishable goods loss rate can generate higher profits for both the port and the shipping company. Furthermore, our findings indicate that BCT investment enhances consumer surplus and social welfare in the maritime supply chain when considering perishable goods loss. Full article

Cold chain logistics possesses unique characteristics, particularly the necessity to maintain low temperatures within containers throughout the distribution process. Real-world traffic conditions, such as congestion, significantly impact the efficiency of cold chain logistics and contribute to increased carbon emissions. To foster green and sustainable development in this sector, a carbon emission trading mechanism has been established, incentivizing companies to invest in energy conservation and emission reduction through economic transactions. This study introduces a multi-objective optimization model for route planning in port seafood logistics, integrating considerations of traffic congestion and zero-carbon transportation. To accurately reflect real-world traffic conditions, a time-dependent function is utilized to model traffic congestion within actual road networks. The road segments are divided, and the travel time for vehicles in each segment is computed. Additionally, the costs associated with the distribution process are analyzed, leading to the development of a multi-objective optimization model aimed at minimizing both distribution costs and zero-carbon transportation costs. The proposed model demonstrates significant economic savings and environmental advantages, providing a theoretical foundation for decision-making processes that support the green and sustainable development of port seafood logistics. Full article