Constrained optimizaton for hospital bed allocation via discrete event simulation with Nested Partitions

This paper aims to further motivate the use of simulation of complex systems in optimizing healthcare operations under uncertainty. One argument to use optimization only such as mathematical programming instead of simulation optimization in making decisions is the ability of the former to account for constraints and to consider a large number of alternatives. However, current state-of-the art of simulation optimization has opened the possibilities of using both simulation and optimization in the case of multiple performance measures. We consider the case of hospital bed allocation and give an example on how a stochastically constrained optimization via simulation can be applied. Nested Partitions are used for the search algorithm and combined with OCBA-CO, an efficient simulation budget allocation, as simulation is time-consuming.