Online strategies for optimizing medical supply in disaster scenarios

In this work we compare several new approaches for optimizing the emergency supply after a major incident online. For a given set of physicians, hospitals and transport vehicles the algorithms introduced in this work compute an assignment of arriving groups of casualties that suffer from specific types of injuries to available transport and medical capacities. We also consider how a patient's individual waiting time until medication impacts the corresponding course of disease and use the concept of penalty functions that can model casualty's health state. We use Simulated Annealing with transition probabilities favoring a balanced workload of vehicles and doctors. We show that using this optimization strategy in combination with a greedy initialization leads to better results compared to using only Greedy or D'Hondt assignment strategy which is currently used in practice.