Optimizing Emergency Supply for Mass Events

In this work we provide an approach for simulating and optimizing the emergency supply in a run-up to a mass event. For a given set of hospitals, transport vehicles and injured suffering from common injuries, our algorithm simulates the workload of provided transport and medication capacities, e.g., doctors. In addition to standard methods, our algorithm considers how a patient's individual waiting time until medication impacts the corresponding course of disease. We use Simulated Annealing with transition probabilities favoring a balanced workload of vehicles and doctors as optimization strategy. We show that using this strategy speeds up convergence and leads to better results compared to Greedy and standard Simulated Annealing with an underlying equal transition probability. Finally, we briefly discuss how different initialization strategies affect the performance of the provided algorithm.