Solutions to address the periodic review inventory control problem with nonstationary random demand, lost sales, and stochastic vendor lead times typically involve making strong assumptions on the dynamics for either approximation or simulation, and applying methods such as optimization, dynamic programming, or reinforcement learning. Therefore, it is important to analyze and evaluate any inventory control policy, in particular to see if there is room for improvement. We introduce the concept of an equilibrium policy, a desirable property of a policy that intuitively means that, in hindsight, changing only a small fraction of actions does not result in materially more reward. We provide a light-weight contextual bandit-based algorithm to evaluate and occasionally tweak policies, and show that this method achieves favorable guarantees, both theoretically and in empirical studies.

该研究介绍了周期性审查库存控制问题的解决方案，涉及非平稳随机需求、销售损失和随机供应商交货时间，引入了平衡策略的概念，并提供了一种轻量级的基于上下文的依据算法，以评估和偶尔调整策略，证明了该方法在理论和实证研究中取得了良好的保证。

用于评估和改进库存控制策略的上下文境决策