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.

The maritime industry, following several conventions, regulations, and initiatives, is trying to adapt and limit its GHG and other local polluting emissions. A crucial aspect of these decarbonisation efforts is the provision of electric energy to vessels, either in port or stationed at anchorages. The latter prevents the option of receiving shore power to support their needs without operating their generators. Research and innovation efforts are attempting to fill this gap through several technology options. This study focuses on the systematic definition of the power and energy demands at anchorage to drive the design of such solutions, focusing on the materialisation of a modular and scalable power barge serving as an offshore power supply solution. Data for various ports and ship types were collected and analysed properly to extract significant insights. Results formulated baselines, per ship type and port, to be used for selecting power modules’ configurations and meeting these demands. This study, considering the lack of industry data regarding power demand, relies on existing studies, guidelines, and other literature to define power demand, which in turn introduces a great deal of uncertainty. Thus, a detailed statistical analysis was employed, together with probability modelling, in order to limit the uncertainty and provide a baseline for the power and energy demands to be verified by future studies capitalising on the accumulation of actual industry data.