同时考虑环境变化与销量缺失，优化零售定价与库存决策。
Learning to Price and Stock Under Contextual and Censored Demand
- 用基函数线性组合建模需求，动态响应市场变化
- 在凹收入条件下实现$\mathcal{O}(K\sqrt{T}\log T)$的后悔上界
- 适合面临复杂需求环境的电商平台和连锁零售
现代零售商面临的关键挑战是做出最优的联合定价与库存控制决策。现实中，需求受多种情境因素影响，且因缺货导致销量丢失，真实需求信息被遮蔽。现有方法往往无法兼顾情境信息与被截断的需求观测。本文提出一种框架，将需求建模为未知系数的基函数线性组合，实现对情境变化的自适应定价与库存决策。我们设计了一种高效算法，在凹收入条件下达到$\mathcal{O}(K\sqrt{T}\log T)$的后悔上界，一般情况下为$\mathcal{O}(K^{2/3}T^{2/3}(\log T)^{1/2})$，并证明下界匹配，表明其最优性。在多种场景下的大规模数值实验验证了该算法的有效性。
原文摘要 · Abstract (English)
To make optimal joint pricing and inventory control decisions is a critical challenge for modern retailers. In practice, retailers face changing market conditions where demands are influenced by various contextual factors, while simultaneously dealing with the difficulty of lost sales that obscure true demand information. However, existing approaches often fail to account for both contextual information and censored demand observations. We address this gap by presenting a framework where we model demand as a linear combination of basis functions with unknown coefficients, allowing for adaptive pricing and inventory decisions that respond to changing contexts. We propose an efficient algorithm to achieve regret bound $\mathcal{O}(K\sqrt{T}\log T)$ under concave revenue conditions and $\mathcal{O}(K^{2/3}T^{2/3}(\log T)^{1/2})$ for the general case, with matching lower bounds confirming optimality. Extensive numerical experiments across diverse scenarios demonstrate our algorithm's effectiveness.
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