Before revision:  We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model . The interpolated value can be computed using a single simulation of this randomized model, irrespective of the number of parameters .
After revision:  We propose a novel technique for embedding such discrete parameters into a continuous space, so that optimization can be performed efficiently using continuous-space methods . Unlike spatial interpolation, our embedding technique is based on a randomization of the simulation model itself can be used to achieve such an embedding when the objective function is a long-run average measure. Unlike spatial interpolation, the computational cost of this embedding is independent of the number of parameters .