Augmented simulation methods for discrete stochastic optimization with recourse
Augmented simulation methods for discrete stochastic optimization with recourse
复制标题
带追索权的离散随机优化的增强仿真方法
DOI：
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发表时间：
2020
影响因子：
4.8
通讯作者：
P. Damien
中科院分区：
文献类型：
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作者：
Tahir Ekin;S. Walker;P. Damien
我们开发了一种增广模拟方法来解决离散随机优化问题，将其转换为随机变量和决策变量联合空间中的大模拟问题。最优决策是通过增广概率模型的模式，使用一个新的多元扩展的经典巴克算法。对具有外生和内生不确定性的单变量和多变量离散报童问题的不同版本进行了详细说明。我们将我们的方法与Metropolis-Hastings算法、基于嵌套采样的增强模拟方法和传统的基于Monte Carlo模拟的优化方案进行了对比。所提出的方法被证明是计算效率高，可以作为另一种工具来解决离散随机优化问题的追索权。
We develop an augmented simulation approach to solve discrete stochastic optimization problems by converting them into a grand simulation problem in the joint space of random and decision variables. The optimal decision is obtained via the mode of the augmented probability model, using a new multivariate extension of the classic Barker’s algorithm. Illustrations on different versions of univariate and multivariate discrete news-vendor problems with exogenous and endogenous uncertainties are detailed. We contrast our method with the Metropolis–Hastings algorithm, the nested sampling-based augmented simulation method, and traditional Monte Carlo simulation-based optimization schemes. The proposed method is shown to be computationally efficient and could serve as another tool to solve discrete stochastic optimization problems with recourse.