Combining strong and screening designs for large-scale simulation optimization

Simulation optimization has received a great deal of attention over the decades, which probably can be attributed to its generality and solvability in many practical problems. On the other hand, simulation optimization is well-recognized as a difficult problem, especially when the problem dimensionality grows. STRONG is a newly-developed method built upon the traditional response surface methodology. Its advantages lie in the automation and provable convergence, as opposed to traditional RSM that requires human involvements and the final solution has no quality guarantee. Moreover, the use of efficient experimental design and regression analysis grants STRONG the great potential to deal with large-scale problems. This paper exploits the basic structure of STRONG and integrates an efficient screening design to handle problems that are of realistic scale, i.e., hundreds of factors. The convergence of the new algorithm is proved. The computational advantage is shown by numerical evaluations.