A Data-Driven Approach to Multistage Stochastic Linear Optimization
A Data-Driven Approach to Multistage Stochastic Linear Optimization
复制标题
数据驱动的多级随机线性优化方法
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Bradley Sturt
中科院分区：
文献类型：
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作者：
D. Bertsimas;Shimrit Shtern;Bradley Sturt
我们提出了一种新的数据驱动的方法来解决未知分布的多阶段随机线性优化问题。该方法包括解决一个强大的优化问题，该问题是从样本路径的基本随机过程。随着样本路径的增加，我们证明了鲁棒问题的最优代价收敛于随机问题的最优代价。据我们所知，这是多阶段随机线性优化的第一个数据驱动方法，当不确定性随时间任意相关时，该方法是渐进最优的。最后，我们开发的近似算法所提出的方法，从强大的优化文献中扩展技术，并通过数值实验程式化的数据驱动的库存管理问题，证明其实用价值。
We propose a new data-driven approach for addressing multi-stage stochastic linear optimization problems with unknown distributions. The approach consists of solving a robust optimization problem that is constructed from sample paths of the underlying stochastic process. As more sample paths are obtained, we prove that the optimal cost of the robust problem converges to that of the underlying stochastic problem. To the best of our knowledge, this is the first data-driven approach for multi-stage stochastic linear optimization which is asymptotically optimal when uncertainty is arbitrarily correlated across time. Finally, we develop approximation algorithms for the proposed approach by extending techniques from the robust optimization literature, and demonstrate their practical value through numerical experiments on stylized data-driven inventory management problems.
影响因子：
2.7
作者：
Hanasusanto, Grani A.;Kuhn, Daniel
通讯作者：
Kuhn, Daniel