# Simulation optimization: A review of algorithms and applications

arXiv: https://arxiv.org/abs/1706.08591 · HTML: https://mlsanity.com/paper/1706.08591/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2017.

## Built on by (most cited first, 23 of 23)

- [Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization](https://mlsanity.com/paper/2211.06149/) (2022)
- [Bayesian Optimisation for Constrained Problems](https://mlsanity.com/paper/2105.13245/) (2021)
- [Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning](https://mlsanity.com/paper/2205.00897/) (2022)
- [Flow-based Extremal Mathematical Structure Discovery](https://mlsanity.com/paper/2601.18005/) (2026)
- [Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays](https://mlsanity.com/paper/2209.01535/) (2022)
- [Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation](https://mlsanity.com/paper/2302.07695/) (2023)
- [Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX](https://mlsanity.com/paper/2303.10672/) (2023)
- [Surrogate Neural Networks for Efficient Simulation-based Trajectory Planning Optimization](https://mlsanity.com/paper/2303.17468/) (2023)
- [An Adaptive Metaheuristic Framework for Changing Environments](https://mlsanity.com/paper/2404.12185/) (2024)
- [Identifying the Best Arm in the Presence of Global Environment Shifts](https://mlsanity.com/paper/2408.12581/) (2024)
- [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)
- [Scaling Bayesian Optimization With Game Theory](https://mlsanity.com/paper/2110.03790/) (2021)
- [Digital Twin Applications in Urban Logistics: An Overview](https://mlsanity.com/paper/2302.00484/) (2023)
- [Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach](https://mlsanity.com/paper/2312.17284/) (2023)
- [Unsupervised Machine Learning Hybrid Approach Integrating Linear Programming in Loss Function: A Robust Optimization Technique](https://mlsanity.com/paper/2408.09967/) (2024)
- [Taking the GP Out of the Loop](https://mlsanity.com/paper/2506.12818/) (2025)
- [Regular Tree Search for Simulation Optimization](https://mlsanity.com/paper/2506.17696/) (2025)
- [GymPN: A Library for Decision-Making in Process Management Systems](https://mlsanity.com/paper/2506.20404/) (2025)
- [A Review on Single-Problem Multi-Attempt Heuristic Optimization](https://mlsanity.com/paper/2509.26321/) (2025)
- [Adaptive Conditional Forest Sampling for Spectral Risk Optimisation under Decision-Dependent Uncertainty](https://mlsanity.com/paper/2603.12507/) (2026)
- [Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI](https://mlsanity.com/paper/2608.19487/) (2026)
- [Principled Design of Expected Improvement for Continuous Simulation Optimization](https://mlsanity.com/paper/2610.18434/) (2026)

## Abstract (arXiv)

Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation---discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise---various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.