Alexandros Koimtzoglou

The increasing trend in the size of commercial ships, regardless of type, combined with the continuous growth of the sea traffic in Greek territorial waters and ports, creates a need to enhance the technical and operational specifications for tugboats. The Greek-owned tugboat fleet in 2019 was lagged behind the average of the respective European fleet in most technical aspects, such as age, bollard pull, and propulsion type. However, the Greek tugboat fleet has been renewed with numerous new tugboats entering the market for tug services especially in the last two years. This paper provides an analysis of the renewed Greek tugboat fleet, comparing its current state at the end of 2024 against 2019 to identify, highlight, and interpret the differences in key technical and operational specifications. Additionally, the paper offers a comparative analysis against the current European tugboat fleet, focusing on the same parameters to highlight the progress made by the Greek fleet in the time period of interest. This paper seeks to identify the primary factors driving the fleet renewal and to demonstrate the benefits this renewal brings to tugboat services in Greek territorial waters and ports.

Piracy and armed robbery continue to pose significant security threats to the shipping industry. This paper presents a real-time threat assessment and crisis classification tool for piracy or armed robbery incidents. The tool is part of a crisis classification module that addresses various categories of security threats. This module is currently being developed as part of the EU-funded research project ISOLA, which aims to introduce an intelligent security superintendence ecosystem. The ecosystem is designed to complement the existing ship security processes and measures applied onboard passenger ships. The tool operates by providing real-time threat classification and subsequent warnings by analysing data collected from the ship’s legacy systems and installed sensors with the utilisation of Bayesian probabilistic techniques, particularly Bayesian Networks. The BN model developed for this purpose is thoroughly examined, and its validation is presented through indicative case studies involving piracy and armed robbery. The main objective is to improve situational awareness, enhance vigilance and early threat detection, and support the decision-making process for the Master and crew, especially under time-sensitive circumstances and stressful conditions.

As can be seen through the continuous research on the Marine Autonomous Surface Ships (MASS) in recent years, several positive as well as optimistic results have been emerged, along with several concerns. In particular, the risk analysis process regarding MASS faces many difficulties as the lack of respective data may lead the estimation of probability or frequency to be extremely uncertain or even biased. To this end, the application of risk informed approaches, that are based on the traditional definition of risk, can be quite misleading. This paper presents a methodology for risk assessment on MASS based on the utilisation of the EAST-BL, a system-based methodology that combines the output of three different networks (i.e., task, social and information) for human and non-human agents of a system to identify hazards and the DNV’s framework for risk assessment from the RBAT study. The objective of the paper is to provide insights regarding the application of EAST-BL on MASS as well as the way that system-based methods can be utilised to enhance the hazard identification and risk assessment processes for MASS.

In case of a ship emergency situation and during its evolvement that might result in an evacuation, the master and the bridge command team of a ship have to continuously assess risk. This is a very complex procedure, as crucial decisions concerning safety are made under time pressure. The use of a decision-support tool would have a positive effect on their performance, resulting in an improvement in the way ships are evacuated. The purpose of this paper is to present the PALAEMON smart risk assessment platform (SRAP). SRAP is a real-time risk assessment platform developed to assist the decision-making process of the master and bridge command team of a ship regarding the evacuation process. Its purpose is to provide decision support for the following aspects: (1) the decision to sound the general alarm (GA) following an accident, (2) monitoring the progress of the mustering process in order to take any additional actions, and (3) the decision to abandon the ship or not. SRAP dynamically assesses the risk to the safety of the passengers and crew members in the different phases of the evacuation process, so one model in the form Bayesian networks (BNs) was developed for each stage of the evacuation process. The results of a case study that was implemented reflect how various parameters such as injuries, congestion, and the functionality of the ship’s systems affect the outcome of each model.