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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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Published in: | Annals of operations research 2016-05, Vol.240 (1), p.351-380 |
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description | 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. |
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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 |
title | Simulation optimization: a review of algorithms and applications |
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