Simulation optimization: a review of algorithms and applications
By Satyajith Amaran, Nikolaos V. Sahinidis, Bikram Sharda, Scott J. Bury. 4OR, 2014
614 citations recorded by the source corpus.
Simulation optimization 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 algebraic model-based 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.
Subfields: Computer Networks and Communications, Management Science and Operations Research, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/06c6b99a25917caa63a164157bf84db97b88c078
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In Computer Networks and Communications
- Richard H. Byrd
- Raghu Bollapragada
- Jie Tan
- Krzysztof Marcin Choromanski
- Vikas Sindhwani
- Jorge Nocedal
In Management Science and Operations Research
- Richard H. Byrd
- Raghu Bollapragada
- Jie Tan
- Krzysztof Marcin Choromanski
- Vikas Sindhwani
- Jorge Nocedal
- Nicolas Loizou
- Peter Richtárik
Citations in the corpus
- Exact and Inexact Subsampled Newton Methods for Optimization (2016, background): “The optimization problem of interest arises in machine learning applications but with appropriate modifications is relevant to other stochastic optimization settings including simulation optimization [2, 12].”
- Optimizing Simulations with Noise-Tolerant Structured Exploration (2018, methodology): “Despite their simplicity, finite difference methods remain the method of choice in the simulation optimization [4] literature, particularly when simulations are computationally inexpensive to run, as is increasingly the case.”