Simulation optimization: a review of algorithms and applications
Simulation optimization: a review of algorithms and applications
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DOI：
10.1007/s10288-014-0275-2
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Satyajith Amaran;N. Sahinidis;B. Sharda;S. Bury
中科院分区：
文献类型：
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作者：
Satyajith Amaran;N. Sahinidis;B. Sharda;S. Bury
仿真优化是指在约束条件下对目标函数的优化，这两者都可以通过随机仿真来评估。为了解决特定模拟的特定特征-离散或连续决策，昂贵或廉价的模拟，单个或多个输出，均匀或异构噪声-文献中提出了各种算法。可以想象，对于每一类问题，都存在几个相互竞争的算法。本文件强调了与基于代数模型的数学规划相比，仿真优化中的困难，参考了该领域最先进的算法，检查和对比了所使用的不同方法，回顾了这些方法解决的一些不同应用，并推测了该领域的未来方向。
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.