Review of Large-Scale Simulation Optimization

TL;DR

This paper reviews techniques for tackling large-scale simulation optimization problems, emphasizing divide and conquer, dimension reduction, gradient methods, and parallelization to address their unique challenges.

Contribution

It provides a comprehensive overview of existing methods and discusses how parallel computing can be leveraged for large-scale SO problems.

Findings

Divide and conquer effectively manages large problem sizes.

Dimension reduction simplifies high-dimensional problems.

Parallelization accelerates solution processes.

Abstract

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.