A Polynomial Time Ambulance Redeployment Policy
In this talk we address an ambulance redeployment problem in which a given number of ambulances have to be dynamically distributed over a set of a set of base locations. The ambulances need to respect norm times to arrive at demand nodes where accidents can occur taking the travel time into account as well. The objective in the problem is to minimise the fraction of ambulances that arrive later than the norm time. We choose our cost function such that the problem is slightly simplified but at the same time leads to polynomial time computable solutions. Additionally, our choice also reduces errors compared to commonly used cost functions that need many sample paths in Monte Carlo simulations. We benchmark our method with the solution of the Maximum Expected Coverage Location Problem, which is a static solution. In a case study with one of the largest ambulance provider regions of the Netherlands, we show that our approach gives a significant improvement on the static MEXCLP solution.
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