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Unformatted text preview: Feature Article d Optimization for Simulation: Theory vs. Practice Michael C. Fu Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, Maryland 20742-1815, [email protected] P robably one of the most successful interfaces between operations research and computer science has been the development of discrete-event simulation software. The recent integration of optimization techniques into simulation practice, specifically into commer- cial software, has become nearly ubiquitous, as most discrete-event simulation packages now include some form of “optimization” routine. The main thesis of this article, how- ever, is that there is a disconnect between research in simulation optimization—which has addressed the stochastic nature of discrete-event simulation by concentrating on theoreti- cal results of convergence and specialized algorithms that are mathematically elegant—and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature (e.g., genetic algo- rithms, tabu search, artificial neural networks). A tutorial exposition that summarizes the approaches found in the research literature is included, as well as a discussion contrast- ing these approaches with the algorithms implemented in commercial software. The article concludes with the author’s speculations on promising research areas and possible future directions in practice. ( Simulation Optimization; Simulation Software; Stochastic Approximation; Metaheuristics ) 1. Introduction Until the end of the last millennium, optimization and simulation were kept pretty much separate in prac- tice, even though there was a large body of research literature relevant to combining them. In the last decade, however, “optimization” routines (the reason for the quotes will be explained shortly) have promi- nently worked their way into simulation packages. That this is a fairly recent development is revealed by the fact that all of the software routines for per- forming simulation optimization listed in the cur- rent edition of Law and Kelton (2000, p. 664, Table 12.11)—AutoStat, OptQuest, OPTIMIZ, SimRunner, INFORMS Journal on Computing © 2002 INFORMS Vol. 14, No. 3, Summer 2002 pp. 192–215 0899-1499/02/1403/0192$5.00 1526-5528 electronic ISSN FU Optimization for Simulation: Theory vs. Practice Table 1 Optimization for Simulation: Commercial Software Packages Optimization Package Vendor Primary Search (Simulation Platform) (URL) Strategies AutoStat AutoSimulations, Inc. evolutionary, (AutoMod) (www.autosim.com) genetic algorithms OptQuest Optimization Technologies, Inc. scatter search and tabu (Arena, Crystal Ball, et al.) (www.opttek.com) search, neural networks OPTIMIZ Visual Thinking International Ltd. neural networks (SIMUL8) (www.simul8.com) SimRunner PROMODEL Corp. evolutionary, (ProModel) (www.promodel.com) genetic algorithms Optimizer...
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This note was uploaded on 11/13/2010 for the course ISE 680 taught by Professor Santanu during the Spring '10 term at Purdue University Calumet.
- Spring '10