A Data-Driven Approach to Multistage Stochastic Linear Optimization
提出一种从样本路径构建鲁棒优化问题的数据驱动方法，解决分布未知的多阶段随机线性优化，给出渐近最优性保证，并通过库存管理实验验证实用性。
We propose a new data-driven approach for addressing multistage 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. We provide asymptotic bounds on the gap between the optimal costs of the robust optimization problem and the underlying stochastic problem as more sample paths are obtained, and we characterize cases in which this gap is equal to zero. To the best of our knowledge, this is the first sample path approach for multistage stochastic linear optimization that offers asymptotic optimality guarantees 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. This paper was accepted by David Simchi-Levi, optimization. Funding: S. Shtern was supported by the Israel Science Foundation [Grant 1460/19]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4352 .