# Surrogate-Based Simulation Optimization

arXiv: https://arxiv.org/abs/2105.03893 · HTML: https://mlsanity.com/paper/2105.03893/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2021.

## Built on by (most cited first, 7 of 7)

- [Bilevel Optimization of Agent Skills via Monte Carlo Tree Search](https://mlsanity.com/paper/2604.15709/) (2026)
- [Contextual Strongly Convex Simulation Optimization: Optimize then Predict with Inexact Solutions](https://mlsanity.com/paper/2512.06270/) (2025)
- [A model aggregation approach for high-dimensional large-scale optimization](https://mlsanity.com/paper/2205.07525/) (2022)
- [SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations](https://mlsanity.com/paper/2511.00685/) (2025)
- [TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems](https://mlsanity.com/paper/2512.24007/) (2025)
- [High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids](https://mlsanity.com/paper/2107.08595/) (2021)
- [Enhanced Derivative-Free Optimization Using Adaptive Correlation-Induced Finite Difference Estimators](https://mlsanity.com/paper/2502.20819/) (2025)

## Abstract (arXiv)

Simulation models are widely used in practice to facilitate decision-making in a complex, dynamic and stochastic environment. But they are computationally expensive to execute and optimize, due to lack of analytical tractability. Simulation optimization is concerned with developing efficient sampling schemes -- subject to a computational budget -- to solve such optimization problems. To mitigate the computational burden, surrogates are often constructed using simulation outputs to approximate the response surface of the simulation model. In this tutorial, we provide an up-to-date overview of surrogate-based methods for simulation optimization with continuous decision variables. Typical surrogates, including linear basis function models and Gaussian processes, are introduced. Surrogates can be used either as a local approximation or a global approximation. Depending on the choice, one may develop algorithms that converge to either a local optimum or a global optimum. Representative examples are presented for each category. Recent advances in large-scale computation for Gaussian processes are also discussed.