Simulation optimization: a review of algorithms and applications
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 mult...
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| Vydané v: | 4OR Ročník 12; číslo 4; s. 301 - 333 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2014
Springer Nature B.V |
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| ISSN: | 1619-4500, 1614-2411 |
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| Abstract | 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. |
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| AbstractList | 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.[PUBLICATION ABSTRACT] 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. |
| Author | Amaran, Satyajith Sahinidis, Nikolaos V. Sharda, Bikram Bury, Scott J. |
| Author_xml | – sequence: 1 givenname: Satyajith surname: Amaran fullname: Amaran, Satyajith organization: Carnegie Mellon University – sequence: 2 givenname: Nikolaos V. surname: Sahinidis fullname: Sahinidis, Nikolaos V. email: niksah@gmail.com, sahinidis@cmu.edu organization: Carnegie Mellon University – sequence: 3 givenname: Bikram surname: Sharda fullname: Sharda, Bikram organization: Engineering and Process Sciences, Core R&D, The Dow Chemical Company – sequence: 4 givenname: Scott J. surname: Bury fullname: Bury, Scott J. organization: Engineering and Process Sciences, Core R&D, The Dow Chemical Company |
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