Efficient simulation-based discrete optimization

In many practical applications of simulation it is desirable to optimize the levels of integer or binary variables that are inputs for the simulation model. In these cases, the objective function must often be estimated through an expensive simulation process, and the optimization problem is NP-hard, leading to a computationally difficult problem. We investigate efficient solution methods for this problem, and we propose an approach that reduces the number of runs of the simulation by using ridge regression to approximate some of the simulation calls. This approach is shown to significantly decrease the computational cost but at a cost of slightly worse solution values.