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Robust optimization in simulation: Taguchi and Response Surface Methodology
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DOI：10.1016/j.ijpe.2009.12.003.png)
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Abstract
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Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization of real (non-simulated) systems. We combine Taguchi's view with RSM for simulated systems. We illustrate the resulting methodology through classic Economic Order Quantity (EOQ) inventory models, which demonstrate that robust optimization may require order quantities that differ from the classic EOQ. (C) 2010 Published by Elsevier B.V.
Keywords:
Pareto frontier
Bootstrap
Latin hypercube sampling
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