针对上下文随机优化中的策略选择难题，提出数据驱动的自适应策略筛选框架。
Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization
- 先构建可行策略库，再用元策略根据上下文信息动态选择最优策略
- 在新货商和运输规划问题中均优于单一策略，尤其在异质场景下表现更优
- 基于交叉验证训练的树集成模型实现完全数据驱动的策略选择
我们研究上下文随机优化（CSO）中的策略选择问题，其中上下文信息作为协变量提供，且决策需满足硬性可行性约束。在许多CSO场景中，不同建模范式产生的多个候选策略在协变量空间中表现出异质性能，无单一策略始终占优。本文提出Prescribe-then-Select（PS）框架：首先构建一组可行候选策略库，然后学习一个元策略，根据观测到的协变量选择最佳策略。元策略通过在训练集上使用交叉验证训练的最优策略树集成实现，使策略选择完全依赖数据。在两个基准CSO问题——单阶段新货商与两阶段运输规划中，PS在协变量空间异质区域持续优于最优单一策略，并在无异质性时收敛至主导策略。所有复现代码见 https://anonymous.4open.science/r/Prescribe-then-Select-TMLR。
原文摘要 · Abstract (English)
We address the problem of policy selection in contextual stochastic optimization (CSO), where covariates are available as contextual information and decisions must satisfy hard feasibility constraints. In many CSO settings, multiple candidate policies--arising from different modeling paradigms--exhibit heterogeneous performance across the covariate space, with no single policy uniformly dominating. We propose Prescribe-then-Select (PS), a modular framework that first constructs a library of feasible candidate policies and then learns a meta-policy to select the best policy for the observed covariates. We implement the meta-policy using ensembles of Optimal Policy Trees trained via cross-validation on the training set, making policy choice entirely data-driven. Across two benchmark CSO problems--single-stage newsvendor and two-stage shipment planning--PS consistently outperforms the best single policy in heterogeneous regimes of the covariate space and converges to the dominant policy when such heterogeneity is absent. All the code to reproduce the results can be found at https://anonymous.4open.science/r/Prescribe-then-Select-TMLR.
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