Large-scale simulation optimization (SO) problems encompass both large-scale ranking-and-selection problems and high-dimensional discrete or continuous SO problems, presenting significant challenges to existing SO theories and algorithms. This paper begins by providing illustrative examples that highlight the differences between large-scale SO problems and those of a more moderate scale. Subsequently, it reviews several widely employed techniques for addressing large-scale SO problems, such as divide and conquer, dimension reduction, and gradient-based algorithms. Additionally, the paper examines parallelization techniques leveraging widely accessible parallel computing environments to facilitate the resolution of large-scale SO problems.
@article{arxiv.2403.15669,
title = {Review of Large-Scale Simulation Optimization},
author = {Weiwei Fan and L. Jeff Hong and Guangxin Jiang and Jun Luo},
journal= {arXiv preprint arXiv:2403.15669},
year = {2024}
}