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Maritime transport forms the backbone of international logistics, as it allows for the transfer of bulk and long-haul products. The sophisticated planning required for this form of transportation frequently involves challenges such as unpredictable weather, diverse types of cargo kinds, and changes in port conditions, all of which can raise operational expenses. As a result, the accurate projection of a ship’s total time spent in port, and the anticipation of potential delays, have become critical for effective port activity planning and management. In this work, we aim to develop a port management system based on enhanced prediction and classification algorithms that are capable of precisely forecasting the lengths of ship stays and delays. On both the training and testing datasets, the XGBoost model was found to consistently outperform the alternative approaches in terms of RMSE, MAE, and R2 values for both the turnaround time and waiting period models. When used in the turnaround time model, the XGBoost model had the lowest RMSE of 1.29 during training and 0.5019 during testing, and also achieved the lowest MAE of 0.802 for training and 0.391 for testing. It also had the highest R2 values of 0.9788 during training and 0.9933 during testing. Similarly, in the waiting period model, the XGBoost model outperformed the random forest and decision tree models, with the lowest RMSE, MAE, and greatest R2 values in both the training and testing phases.

With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for analytics from diverse sources like shipping routes, weather conditions, historical incidents, and cargo specifications has become a challenging task in the distribution environment. This challenge gives rise to the possibility of faulty products and traditional testing techniques fall short of achieving optimal performance. To address this issue, we propose a novel decentralised software system based on Transfer Learning and blockchain technology named as BETL (Blockchain -Enabled Transfer Learning). Our proposed system aims to automatically detect and prevent vulnerabilities in maritime operational data by harnessing the power of transfer learning and smart contract-driven blockchain. The vulnerability detection process is automated and does not rely on manually written rules. We introduce a non-vulnerability score range map for the effective classification of operational factors. Additionally, to ensure efficient storage over the blockchain, we integrate an InterPlanetary File System (IPFS). To demonstrate the effectiveness of transfer learning and blockchain integration for secure logistic management, we conduct a testbed-based experiment. The results show that this approach can achieve high precision (98.00%), detection rate (98.98%), accuracy (97.90%), and F-score (98.98), which highlights its benefits in enhancing the safety and reliability of maritime logistics processes. Additionally, the computational time of BETL (the proposed approach) was improved by 18.9% compared to standard transfer learning.

Logistics centres are currently performing a key function in the development of countries through their ability to regulate goods, markets, and transport. This is shown by the infrastructure, cost, goods flow, and quality of logistical services provided by these centres. Nevertheless, in developing nations or regions with antiquated logistics infrastructure, conventional logistics centres seem to struggle to manage the volume of commodities passing through them, resulting in persistent congestion and an unsteady flow of goods inside these facilities. This issue poses a challenge to the progress of any nation. The emergence of new technology offers a potential avenue to solve the problems inherent in traditional logistics centres. Most prominently, four technologies (the Internet of Things (IoT), Blockchain, Big Data and Cloud computing) are widely applied in traditional logistics centres. This work has conducted a thorough analysis and evaluation of these new technologies in relation to their respective functions and roles inside a logistics centre. Furthermore, this work proposes difficulties in applying new technologies to logistics centres related to issues such as science, energy, cost, or staff qualifications. Finally, future development directions, related to expanding policies in technological applications, or combining each country’s policies for the logistics industry, are carefully discussed.

The global supply chain has been growing strongly in recent years. This development brings many benefits to the economy, society, and human resources in each country but also causes a large number of concerns related to the environment since traditional logistics activities in the supply chain have been releasing a significant amount of emissions. For that reason, many solutions have been proposed to deal with these environmental pollution problems. Among these, three promising solutions are expected to completely solve environmental problems in every supply chain: (i) Application of blockchain in the supply chain, (ii) Use of renewable energy and alternative fuels, and (iii) Design of a closed supply chain. However, it seems to lack a comprehensive study of these solutions aiming to overcome the drawbacks of traditional logistics. Indeed, this work focuses on analyzing and evaluating the three above-mentioned solutions and the impacts of each solution on solving problems related to traditional logistics. More importantly, this work also identifies critical factors and challenges such as policies, laws, awareness, and risks that are found to be remarkable difficulties in the shifting progress of traditional logistics to green logistics. Finally, directions for developing and deploying green solutions to the logistics, supply chain, and shipping sectors toward decarbonization strategies and net-zero goals are discussed in detail.

Recently, because of serious global challenges including the consumption of energy and climate change, there has been an increase in interest in the environmental effect of port operations and expansion. More interestingly, a strategic tendency in seaport advancement has been to manage the seaport system using a model which balances environmental volatility and economic development demands. An energy efficient management system is regarded as being vital for meeting the strict rules aimed at reducing the environmental pollution caused by port facility activities. Moreover, the enhanced supervision of port system operating methods and technical resolutions for energy utilisation also raise significant issues. In addition, low-carbon ports, as well as green port models, are becoming increasingly popular in seafaring nations. This study comprises a comprehensive assessment of operational methods, cutting-edge technologies for sustainable generation, storage, and transformation of energy, as well as systems of smart grid management, to develop a green seaport system, obtaining optimum operational efficiency and environmental protection. It is thought that using a holistic method and adaptive management, based on a framework of sustainable and green energy, could stimulate creative thinking, consensus building, and cooperation, as well as streamline the regulatory demands associated with port energy management. Although several aspects of sustainability and green energy could increase initial expenditure, they might result in significant life cycle savings due to decreased consumption of energy and output of emissions, as well as reduced operational and maintenance expenses.

In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

Due to recent emission-associated regulations imposed on marine fuel, ship owners have been forced to seek alternate fuels, in order to meet the new limits. The aim of achieving low-carbon shipping by the year 2050, has meant that alternative marine fuels, as well as various technological and operational initiatives, need to be taken into account. This article evaluates and examines recent clean fuels and novel clean technologies for vessels. The alternative fuels are classified as low-carbon fuels, carbon-free fuels, and carbon neutral fuels, based on their properties. Fuel properties, the status of technological development, and existing challenges are also summarised in this paper. Furthermore, researchers have also investigated energy-saving devices and discovered that zero-carbon and virtually zero-carbon clean fuels, together with clean production, might play an important part in shipping, despite the commercial impracticability of existing costs and infrastructure. More interestingly, the transition to marine fuel is known to be a lengthy process; thus, early consensus-building, as well as action-adoption, in the maritime community is critical for meeting the expectations and aims of sustainable marine transportation.

Electrical equipment is increasingly diversified in both types and capacity to meet the maximum needs of people in the 4th industrial revolution. This development has helped people to achieve many great scientific achievements, but this development has led to a rapid increase in the demand for electric energy in recent years. The traditional electricity supply from fossil fuels is gradually depleting, which has prompted the search for clean and renewable energy sources to gradually replace the dependence on this energy source. Prosumer, HEMS (home energy management system), and other solutions have been researched and applied to optimize electrical energy sources. However, for countries that mainly use fossil energy sources like Vietnam, these solutions are not effective. Policy on the management could help to solve this problem, in particular, the price policy is the solution that Vietnam has used to effectively manage this energy source. This article analyzes the issues of applicable pricing policy in Vietnam, proposes potential policies to improve and protect the electric energy system, as well as enhances the rate of renewable energy use in the electricity system in Vietnam

Predictive analytics utilizing machine learning algorithms play a pivotal role in various domains, including the profiling of carbon dioxide (CO2) emissions. This research paper delves into an extensive exploration of different algorithms, encompassing neural networks with diverse architectures, optimization, training, ensemble, and specialized algorithms. The primary objective of this research is to evaluate the efficacy of supervised and unsupervised algorithms, including Deep Belief Networks, Feed Forward Neural Networks, Gradient Boosting, and Regression, as well as Convolutional Neural Networks, Gaussian, Grey, and Markov models, and clustering and optimization algorithms. The study places particular emphasis on data-driven methodologies and cross-validation techniques with an evaluation of the learning models entailing comprehensive training, validation, and testing, employing evaluation metrics such as R2, MAE, and RMSE. The study employs correlation analysis to examine the relationship between input parameters and emission characteristics. The research highlights the advantageous attributes of these algorithms in accurately forecasting CO2 emissions, evaluating energy sources, improving prediction accuracy, and estimating emissions. Notably, deep learning, Artificial Neural Networks (ANN), and Support Vector Machines (SVM) demonstrate effectiveness across diverse industries, while the Modified Regularized Fast Orthogonal-Extreme Learning Machine (MRFO-ELM) algorithm optimizes predictions specifically related to coal chemical emissions. Hybrid techniques demonstrate accuracy in predicting carbon emissions and energy consumption, whereas gray prediction models provide reliable estimates even with limited data. However, it is important to acknowledge certain limitations, including data requirements, potential inaccuracies arising from complex factors, constraints faced by developing countries, and the impact of electric vehicle expansion on the power grid. To optimize models, a survey is conducted, involving customization of parameters and learning rates, while exploring various performance metrics to evaluate model accuracy. The research outcomes contribute to the effective monitoring of CO2 emissions in operational environments, thereby aiding executive decision-making processes. [ABSTRACT FROM AUTHOR]

This study incorporates two unique machine learning algorithms, Huber regression and Light Gradient Boosting Machines (LGBM), for estimating ship consumption of fuel. These methods are employed to create forecasting models for ship fuel consumption during journeys, which is especially useful when interacting with non-linear data. The study then analyzes and evaluates the prediction accuracy of these two approaches compared to a baseline model generated using linear regression. The results of the investigation show that both methods establish extremely accurate predictions while handling non-linear data quickly. However, the Huber-based model outperforms the LGBM in terms of prediction accuracy, with an R-squared value of 0.979 versus 0.917 for the LGBM. In addition, the Huber-based model has a diminished prediction error, with an RMSE of 2.278, compared to the LGBM model's RMSE of 4.55. The graphical methods of the violin plot and Taylor’s diagram further established the superiority of Huber ML. These findings imply that Huber regression could be a suitable option for estimating in-route ship fuel usage in real time. As a consequence, this study emphasises the potential benefits of machine learning for accurately predicting ship fuel consumption, providing encouraging possibilities to optimise fuel usage while lowering greenhouse gas emissions. [ABSTRACT FROM AUTHOR]