Metamodel-Based Robust Simulation-Optimization: An Overview
Metamodel-Based Robust Simulation-Optimization: An Overview
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DOI：
10.1007/978-1-4899-7547-8_2
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发表时间：
2015
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影响因子：
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通讯作者：
G. Dellino;J. Kleijnen;C. Meloni
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文献类型：
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作者：
G. Dellino;J. Kleijnen;C. Meloni
模拟系统的优化是许多技术的目标，但它们中的大多数假设已知的环境。最近，“稳健”的不确定环境会计方法已经开发出来。鲁棒优化处理受不确定性影响的问题，提供在某种意义上对模型参数的扰动不敏感的解决方案。已经提出了几种替代方法来实现基于仿真的优化问题的鲁棒性，采用不同的实验设计和/或元建模技术。本章回顾了基于模拟系统的鲁棒优化方法的当前发展状况。首先，我们概述了鲁棒数学规划。然后，我们讨论田口的方法在20世纪70年代推出。最后，我们考虑使用元模型来解决鲁棒性的方法，特别是克里格法。建议的方法使用田口的不确定世界的观点，但取代他的统计技术克里金。我们说明了由此产生的方法，通过基本的库存模型。
Optimization of simulated systems is the goal of many techniques, but most of them assume known environments. Recently, “robust” methodologies accounting for uncertain environments have been developed. Robust optimization tackles problems affected by uncertainty, providing solutions that are in some sense insensitive to perturbations in the model parameters. Several alternative methods have been proposed for achieving robustness in simulation-based optimization problems, adopting different experimental designs and/or metamodeling techniques. This chapter reviews the current state of the art on robust optimization approaches based on simulated systems. First, we summarize robust Mathematical Programming. Then we discuss Taguchi’s approach introduced in the 1970s. Finally, we consider methods to tackle robustness using metamodels, and Kriging in particular. The proposed methodology uses Taguchi’s view of the uncertain world, but replaces his statistical techniques by Kriging. We illustrate the resulting methodology through basic inventory models.