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.
Abstract. This paper deals with the problem of effective test suite reduction. In its original form this problem is equivalent to the set covering problem, which has already been extensively studied and many strategies such as greedy or branch and bound for computation of an approximative optimal solution to this NP-complete problem are known. All of these algorithms only focus on one objective which is the minimization of the number of action calls within the test suite reduction. However, practical experience shows that balancing out the distribution of action calls is another objective which should be considered when choosing an efficient test suite. We will therefore introduce and evaluate different extensions of the standard techniques which incorporate action call distribution. We will see that these adjusted strategies can compute a reduced test suite with a smoother distribution over function calls within an acceptable amount of additional time in comparison to the classic algorithms.
Abstract-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 sufTer 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.
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