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.

翻译：针对具有非平稳随机需求、销售损失及随机供应商提前期的周期性盘点库存控制问题，现有解决方案通常需对动态系统做出强假设（用于近似或仿真），并采用优化、动态规划或强化学习等方法。因此，分析并评估任何库存控制策略至关重要，尤其是判断其是否存在改进空间。我们提出了“均衡策略”这一概念——这一策略的理想性质直观表现为：事后看来，仅改变少量决策动作不会带来显著回报提升。我们设计了一种轻量级基于上下文的赌博机算法，用于评估策略并偶尔进行调整，理论分析与实证研究均表明该方法具有优越的保障性能。