As global energy production progressively turns toward a green environment and economy, one of the safety challenges to the maritime industry that has arisen lies within offshore wind farms (OWFs). As renewable sources of energy whose numbers are rapidly expanding, their impact to the safety of navigation of the ships that navigate in their vicinity ought to be examined further. An ever-growing number of OWFs has led to safety concerns that have never been taken into consideration before. This article gives a structured quantitative analysis and an in-depth review of the literature connected to the safety of navigation, collision probability, and risk assessment that OWFs pose to all maritime industry agents. In this article, the main concerns of the impact of OWFs to the safety of navigation are analyzed using a combination of both the PRISMA and PICOC methodologies. Various types of scientific papers such as journal articles, conference proceedings, MSc theses, PhD theses, and online works of research are collated into a detailed bibliometric analysis and categorized by the most relevant parameters providing valuable perspectives on the current state of art in the field. The findings of this research emphasize the need for a further and more thorough analysis on the theoretical installment of OWFs and their inevitable impact on increasing maritime traffic complexity. The results of this article can form a strong basis for further scientific development in the field and can give useful insights to all maritime industry stakeholders dealing with OWFs.

This study investigates the application of different neural network (NN) models in assessing the risk of the detention of offshore vessels during port state control (PSC) inspections. The focus is on the use of different NN models (“nnet”, “mlp”, “neuralnet”, “rsnns”) to identify the main risk factors based on historical data on vessels and their inspections. The main objective of this research is to improve maritime safety and the efficiency of inspection procedures by applying techniques that can more accurately predict the probability of detention of the offshore vessels. These models make it possible to analyse complex patterns in the data, such as the relationships between the country of inspection, flag, memorandum, age, tonnage and previous deficiencies, and the risk of detention. Understanding these patterns is crucial for inspection teams’ proactive action as it helps direct resources to potentially high-risk vessels. Implementing these models into PSC processes helps to optimise resource allocation, reduce unnecessary costs, and increase the reliability of decision-making processes. NN models significantly help in recognising non-linear patterns and provide high accuracy in risk prediction. The study also includes a comparative analysis of the elements that determine the accuracy, sensitivity, and other performance aspects of the models to determine the most appropriate approach for practical implementation. The results emphasise the importance of applying artificial intelligence (AI) in various aspects of modern maritime safety management. This research opens up new opportunities for the development of intelligent support systems that not only increase safety but also improve the efficiency of inspection processes on a global scale.

This article examines the methods and application of classification trees and multi-criteria decision-making in the process of holding offshore vessels in port (Port State Control—PSC). This work aims to improve the efficiency and precision of the control processes in the detention of offshore vessels by using advanced analytical methods. Methodologically, a classification decision tree was used to identify the most important risk factors, enabling a faster and more accurate assessment of the possibility of detaining offshore vessels in port. Multi-criteria decision-making (MCDM) also enabled the simultaneous assessment of multiple factors, ensuring a balanced, robust, accurate, and objective approach. The research results show that the integration of these methods into the PSC process can significantly increase the safety of shipping and reduce the operating costs of offshore vessels. The application of these analytical tools can lead to a more systematic and transparent inspection process. This paper suggests further research and training of inspectors in the use of these techniques to maximize their applicability and effectiveness. Finally, this paper emphasizes the potential of classification trees and MCDM for safer and more efficient maritime transport by improving PSC procedures.

This literature review provides a structured quantitative analysis of existing research on the application of machine learning models (MLMs) and multi-criteria decision-making methods (MCDM) in the context of port state control (PSC). The aim of the study is to capture current research trends, identify thematic priorities, and demonstrate how these analytical tools have been used to support decision-making and risk assessment in the maritime domain. Rather than evaluating the effectiveness of individual models, the study focuses on the distribution and frequency of their use and provides insights into the development of methodological approaches in this area. Although several studies suggest that the integration of MLMs and MCDM techniques can improve the objectivity and efficiency of PSC inspections, this report does not provide a comparative assessment of their performance. Instead, it lays the groundwork for future qualitative studies that will assess the practical benefits and challenges of such integration. The findings suggest a fragmented but growing research interest in data-driven approaches to PSC and highlight the potential of advanced analytics to support maritime safety and regulatory compliance.

Dynamic positioning systems ensure additional position accuracy in different operations in the maritime industry, such as shuttling, drilling, diving, pipe laying, and others. Although they are known to be robust systems, they are not exempt from failures leading to incidents, among which we can find human-related incidents. This research analyzes 62 human-related dynamic-positioning incidents and aims to determine which segment in the dynamic positioning system influences these occurrences by applying binary logistic regression modeling techniques in the test sample and validating the results using a control sample. The results indicate that thrusters have the most significant influence on human-related incidents; however, not all DP operations are affected in the same measure. By stratifying the database and considering the different operations in progress, it is noted that human-related incidents while drilling operations are in progress are effectively influenced using a higher percentage of thrusters online.

Over the past century, with an accelerated increase in world energy demand, oil remained the most intriguing energy source, while oil exploration and production evolved from an onshore to an offshore exploration and production facility. In logistic and operational planning, the supply of offshore installations is the primary dependent of offshore supply vessels (OSVs). OSVs are classified into two main specialized groups, Anchor Handling Tug Supply (AHTS) and Platform Supply Vessels (PSVs). The establishment and maintenance of offshore oil installations require considerable financial resources for the hire of OSVs, which increases the oil exploration and production operator’s overall budget. Planning the offshore fleet structure aims to reduce expenses and increase the utilization of OSVs. This work will analyze the actual data of the southwestern African offshore market based on 24/7 working time and the use analysis of marine activities to define the parameters of the offshore fleet structure. The research results will be used to develop a methodology for modeling the optimal infrastructure of the offshore fleet. With statistical data of all support vessels’ activities and the use of linear programming (LP), it is possible to determine the vessel’s employment pattern and the optimal fleet structure by type and number of OSVs.