  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.
