A distributed computing architecture for simulation and optimization

We design a generic framework to integrate distributed simulation and optimization models. Many problems require the integration of these two types of models. For example, stochastic programming can use simulation models as a scenario generator for optimization models; in some other cases, simulation models need optimization models to help determine system parameters. The framework is shown to be able to provide various services to help the integration of simulation and optimization models. We illustrate our implementation with a product-mix example. The example integrates a discrete event simulation of a product-mix problem with a linear programming (optimization) model of such a system. The simulation updates the parameters in the optimization model, which as a result will generate a new production plan.