Learning to Price and Stock Under Contextual and Censored Demand

2026-09-05

AI Summary

本文解决了零售商在多变市场条件下的定价和库存控制问题，通过结合上下文信息和需求审查的模型，并提出了一种有效算法以优化决策。

Abstract

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.

Problem

• contextual factors
• censored demand
• joint pricing and inventory control

Innovation

• contextual demand
• censored observations
• adaptive pricing and inventory control
• efficient algorithm
• regret bounds