Resource Optimization in Mass Casualty Management: A Comparison of Methods

This paper studies and compares various optimization approaches ranging from classical optimization to machine learning to respond swiftly and optimally in casualty incidents. Key points of interest in the comparison are the solution quality and the speed of finding it. In multiple-casualty scenarios knowing both is essential to choosing the correct method. A set of 960 synthetic MCI scenarios of different settings are being considered here to give an indication of scalability. For these scenarios, the aim is to optimize the number of victims receiving specialized treatments at the nearest available hospital.