Simulation Optimization is providing solutions to important practical problems previously beyond reach. Specifically, in applications involving risk and uncertainty, Simulation Optimization surpasses the capabilities of other optimization methods, not only in the quality of solutions, but also in their interpretability and practicality.
Traditional scenario-based approaches to optimization, such as scenario optimization and robust optimization, are effective in finding a solution that is feasible for all the scenarios considered, and minimizing the deviation of the overall solution from the optimal solution for each scenario. These approaches, however, only consider a very small subset of possible scenarios, and the size and complexity of models they can handle are very limited. Simulation gives the limitless power to try various what-if scenarios. Optimization can suggest optimal scenarios, suggesting the what-if scenarios that may yield the best results.
