Hospital evacuation in large-scale disasters using limited aerial transport resources

Hospitals play a key role in providing medical assistance in a post-disaster phase. However, depending on the nature, scale, and severity of disasters, they may also be affected and may need to evacuate their patients. Current evacuation response plans for hospitals are predominantly based on the use of land vehicles, whereas little methodological advancements have been reported for when aerial hospital evacuations become necessary. To address this problem, this study develops a model that facilitates evacuation planning for moving patients from at-risk hospitals to remote locations using limited aerial transportation resources. A subset of the model parameters is treated as stochastic in order to reflect the uncertainties involved in real-world emergency conditions. The complexities of the model are handled via the use of a metaheuristic approach, the Variable neighbourhood search (VNS) algorithm, to provide a suitable solution within a reasonable amount of time. A relaxed mathematical model is used in the proposed VNS to generate an initial solution, which is then transformed into a feasible solution through a try-and-error procedure. The proposed method applies a reinforcement learning procedure that uses different local search strategies within the loop of the VNS. A hypothetical evacuation scenario of real-life scale, from an island state in Australia to the mainland states, is used to evaluate the ability of the model to generate efficient plans. In this case study, Tasmania is considered as an evacuation point, while hospitals located in other major cities host evacuated patients. Comparing the proposed method with a conventional method shows superior performance based on all relevant metrics. Emergency response agencies across many countries that can adopt the proposed methodology, when facing large-scale emergencies caused by wars, epidemics, or natural hazards such as wildfires and earthquakes.