Work

Review of Large-Scale Simulation Optimization

Year: 2025

Type: article

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.

Source: Journal of the Operations Research Society of China

DOI: 10.1007/s40305-025-00599-8

Authors: Wei-Wei Fan, L. Jeff Hong, Guang-Xin Jiang, Jun Luo

Institutions: Tongji University, University of Minnesota, Harbin Institute of Technology, Shanghai Jiao Tong University

Language: English

Topic: Simulation Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,290

Funder: National Natural Science Foundation of China