在上下文和删失需求下的定价与库存联合决策学习
Learning to Price and Stock Under Contextual and Censored Demand
- The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对零售商面临上下文影响和删失需求下的联合定价与库存控制问题，提出将需求建模为基函数线性组合的框架，并设计高效算法，在凹收益和一般条件下分别达到最优遗憾界，实验验证其有效性。
AI中文摘要：
做出最优的联合定价与库存控制决策是现代零售商面临的关键挑战。在实践中，零售商面临不断变化的市场条件，需求受到各种上下文因素的影响，同时还需应对因销售损失而掩盖真实需求信息的困难。然而，现有方法往往未能同时考虑上下文信息和删失需求观测。我们通过提出一个框架来解决这一空白，在该框架中，我们将需求建模为具有未知系数的基函数的线性组合，从而允许自适应定价和库存决策以响应变化的上下文。我们提出了一种高效算法，在凹收益条件下实现了遗憾界 $\mathcal{O}(K\sqrt{T}\log T)$，在一般情况下实现了 $\mathcal{O}(K^{2/3}T^{2/3}(\log T)^{1/2})$，并匹配下界以确认最优性。跨多种场景的大量数值实验证明了我们算法的有效性。
英文摘要：
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.
