Simulation optimization: a review of algorithms and applications

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...

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Bibliographic Details
Published in:Annals of operations research 2016-05, Vol.240 (1), p.351-380
Main Authors: Amaran, Satyajith, Sahinidis, Nikolaos V., Sharda, Bikram, Bury, Scott J.
Format: Article
Language:English
Subjects:
Algebra
Algorithms
Business and Management
Combinatorics
Computer simulation
Decision-making
Engineering
Expected values
Management research
Mathematical analysis
Mathematical functions
Mathematical models
Mathematical optimization
Mathematical programming
Methods
Operations research
Operations Research/Decision Theory
Optimization
R&D
Random variables
Research & development
SI: 4OR Surveys
Simulation
Simulation methods
Software
State of the art
Studies
Theory of Computation
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Summary: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 SO 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.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-015-2019-x