Data-driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment
Data-driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment
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在不断变化的环境中利用易腐烂库存进行数据驱动的动态定价和订购
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发表时间：
2020
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通讯作者：
Jing
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文献类型：
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作者：
N. B. Keskin;Yuexing Li;Jing
我们考虑一个零售商，销售易腐产品，使联合定价和库存订购决策在有限的时间范围内的T期间的销售损失。探索一个领先的连锁超市的现实生活中的数据集，我们确定了这样的零售商所面临的几个独特的挑战，还没有在文献中共同研究：零售商没有完美的信息（1）需求价格关系，（2）需求噪声分布，（3）库存易腐率，（4）需求价格关系如何随时间变化。此外，需求噪声分布对于某些产品是非参数的，但对于其他产品是参数的。为了应对这些挑战，我们设计了两种类型的数据驱动的定价和订购（DDPO）政策的情况下，非参数和参数噪声分布。通过后悔（regret），即不知道（1）-（4）所造成的利润损失来衡量业绩，我们证明了在非参数和参数噪声分布的情况下，我们的DDPO策略的T期后悔分别为[公式：见正文]和[公式：见正文]的顺序。这些是在这些环境中后悔的最佳增长率（对数项）。在上述现实生活数据集的背景下实施我们的政策，我们表明，我们的方法显着优于超市连锁店的历史决策。此外，我们表征参数制度，量化的相对重要性的变化的环境和产品易腐性。最后，我们扩展了我们的模型，允许年龄相关的易腐性和需求审查，并修改我们的政策来解决这些问题。这篇论文被大卫Simchi-Levi，管理科学数据驱动规范分析特别部分接受。
We consider a retailer that sells a perishable product, making joint pricing and inventory ordering decisions over a finite time horizon of T periods with lost sales. Exploring a real-life data set from a leading supermarket chain, we identify several distinctive challenges faced by such a retailer that have not been jointly studied in the literature: the retailer does not have perfect information on (1) the demand-price relationship, (2) the demand noise distribution, (3) the inventory perishability rate, and (4) how the demand-price relationship changes over time. Furthermore, the demand noise distribution is nonparametric for some products but parametric for others. To tackle these challenges, we design two types of data-driven pricing and ordering (DDPO) policies for the cases of nonparametric and parametric noise distributions. Measuring performance by regret, that is, the profit loss caused by not knowing (1)–(4), we prove that the T-period regret of our DDPO policies are in the order of [Formula: see text] and [Formula: see text] in the cases of nonparametric and parametric noise distributions, respectively. These are the best achievable growth rates of regret in these settings (up to logarithmic terms). Implementing our policies in the context of the aforementioned real-life data set, we show that our approach significantly outperforms the historical decisions made by the supermarket chain. Moreover, we characterize parameter regimes that quantify the relative significance of the changing environment and product perishability. Finally, we extend our model to allow for age-dependent perishability and demand censoring and modify our policies to address these issues. This paper was accepted by David Simchi-Levi, Management Science Special Section on Data-Driven Prescriptive Analytics.