Simulation optimization: a review of algorithms and applications

• Published in: 4OR Vol. 12; no. 4; pp. 301 - 333
• Main Authors: Amaran, Satyajith, Sahinidis, Nikolaos V., Sharda, Bikram, Bury, Scott J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2014; Springer Nature B.V
• Subjects: Algebra; Algorithms; Business and Management; Industrial and Production Engineering; Invited Survey; Mathematical functions; Mathematical programming; Operations research; Operations Research/Decision Theory; Optimization; R&D; Random variables; Research & development; Simulation; Software; 90-02 Operations research, mathematical programming: Research exposition (monographs, survey articles); 90C56 Derivative-free methods and methods using generalized derivatives; Simulation optimization; Optimization via simulation; Derivative-free optimization; 65-02 Numerical analysis: Research exposition (monographs, survey articles)
• ISSN: 1619-4500; 1614-2411
• DOI: 10.1007/s10288-014-0275-2

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.