# Simulation optimization: A review of algorithms and applications

## TL;DR

This paper reviews various algorithms for simulation optimization, highlighting challenges, comparing approaches, and discussing applications and future research directions in the field.

## Contribution

It provides a comprehensive overview of existing simulation optimization algorithms, analyzing their features, applications, and potential future developments.

## Key findings

- Comparison of different algorithm classes
- Discussion of challenges in simulation optimization
- Overview of diverse application areas

## Abstract

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.