An agent-based modelling framework for performance assessment of search and rescue operations in the Barents Sea

Abstract

In recent years, the increase in Arctic offshore activities has raised concerns about the search and rescue (SAR) operations as mitigative measures to ensure the safety of shipping and cruise activities. Performing SAR operations in the remote Arctic offshore environment is exceptionally challenging due to the severe environmental conditions, including low temperatures, high waves, strong winds, heavy snow showers, sea ice, spray icing, dense fog, limited visibility, and polar low pressures. Moreover, the scarcity of port infrastructure along the Arctic coastline exacerbates the difficulties faced during SAR operations. Tackling the aforementioned challenges necessitates a comprehensive modelling framework for analysis of SAR operations in the Arctic that is able to consider the dynamics and uncertain nature of Arctic harsh environmental conditions, and the constraints imposed by the limited capacity of Arctic SAR infrastructure. To this aim, this paper proposes an agent-based modelling (ABM) framework to assess the performance of SAR operations while tackling such challenges in the Arctic. A Monte Carlo simulation approach is used to model the dynamics and uncertainty of weather and sea conditions using historical data, using which some severity levels are determined. Expert judgement process is then used quantify the impacts of such severity levels on the performance of rescue agents, and thus, on the total rescue time. To validate the proposed framework, an illustrative case in the Norwegian Barents Sea is considered, where the performance of SAR infrastructure is examined under different rescue scenarios. According to the results of this paper, the total rescue time is the longest during December to February, and the shortest rescue time is associated with months May to August. Some recommendations are further proposed to improve the performance of SAR infrastructure in the Barents Sea.