Our optimization strategy relies on the cheaper but less accurate surrogate level to perform most of the optimization, using the more accurate and expensive simulation level only to build the surrogates and validates proposed surrogate-level solutions. The optimization framework is illustrated in Figure . Global optimization is then performed at the surrogate level, utilizing the speedup gained with surrogate-level fast objective evaluation. Once this proposes a candidate vector of optimized parameters, the objective function for that parameter vector is evaluated at the simulation level. If the simulation-level objective is lower than the simulation objective for the previous parameter vector, the new parameter vector is accepted as an improved solution; if not, it is rejected. Regardless of acceptance or rejection, the surrogate model is rebuilt with the information from the simulation level evaluation. This process is then repeated until a maximum number of simulation optimizations is reached, a convergence criteria is met, or the optimizer cannot find an improved solution.