English(EN) Learning to Price and Stock Under Contextual and Censored Demand

新框架解决情境化、审查需求下的定价和库存问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一个新框架，以应对零售商在不断变化的市场条件和模糊的需求数据下面临的最优定价和库存控制的挑战。所提出的模型将需求视为具有未知系数的基函数的线性组合，从而能够做出响应情境因素的自适应决策。设计了一个高效的算法来实现强大的遗憾界限，数值实验验证了其在各种场景下的有效性。 AI

影响这项研究通过更好地处理复杂的需求场景，为优化零售运营提供了一种新颖的算法方法。

排序理由该项目是一篇在arXiv上发表的学术论文，详细介绍了一个针对机器学习特定问题的新框架和算法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Zean Han, Zezhen Ding, Jiheng Zhang · 2026-09-10 04:00

在情境化和审查需求下学习定价和库存

arXiv:2609.06083v1 Announce Type: new 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 sim…