Review of Large-Scale Simulation Optimization

doi:10.1007/s40305-025-00599-8

Abstract

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.

Key words: Simulation optimization, Large-scale problems, Ranking and selection, Dimension reduction, Gradient-based algorithms, Parallel algorithms

CLC Number:

Wei-Wei Fan, L. Jeff Hong, Guang-Xin Jiang, Jun Luo. Review of Large-Scale Simulation Optimization[J]. Journal of the Operations Research Society of China, 2025, 13(3): 688-722.

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