Simulation optimization: a review of algorithms and applications

• Published in: Annals of operations research Vol. 240; no. 1; pp. 351 - 380
• Main Authors: Amaran, Satyajith, Sahinidis, Nikolaos V., Sharda, Bikram, Bury, Scott J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2016; Springer; Springer Nature B.V
• 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; Simulation optimization; Optimization via simulation; Derivative-free optimization
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-015-2019-x

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.