Modelling a major maritime disaster scenario using the universal modelling framework for sequential decisions

This article examines a major maritime disaster scenario in which a large number of individuals in medical distress are stranded at a remote Arctic location and must be evacuated. Within this context, we examine a multi-domain operation where a maritime asset with limited capacity is tasked to provide medical assistance at the evacuation site and limited air assets are used to evacuate individuals to a forward operating location. The objective of the operation is to maximize the number of lives saved. However, this value is dependent on a variety of factors, such as: uncertainty in the deterioration and improvement of the individuals' medical condition; the arrival time of the maritime asset; the number of air assets employed; and the decision policies used to select who receives medical assistance and who is evacuated. Our contributions are twofold. First, we formulate the multi-domain operation as a sequential decision problem using the universal modelling framework for sequential decisions. Second, we present a hypothetical case study that explores how this model may be used to provide decision support for planning purposes. To do so, we use the model to explore how the expected number of lives saved is impacted by: varying the maritime asset's arrival time and number of air assets employed; varying the decision policies used and assessing how these impacts are dependent upon when the maritime asset arrives; and varying the arrival time of the air assets, such as when they are prepositioned.

Automated summary

This article examines a major maritime disaster scenario and explores the evacuation process in such a situation. The study finds that there are various factors that affect the number of lives saved, including the uncertainty of individuals' medical condition, the arrival time of maritime and air assets, and the decision policies used. The authors formulate the multi-domain operation as a sequential decision problem using a modeling framework and provide decision support through a hypothetical case study.