Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment

易腐性 后悔 非参数统计 动态定价 背景(考古学) 计算机科学 参数统计 计量经济学 经济 运筹学 微观经济学 数学 业务 营销 统计 机器学习 古生物学 生物
作者
N. Bora Keskin,Yuexing Li,Jing‐Sheng Song
出处
期刊:Management Science [Institute for Operations Research and the Management Sciences]
卷期号:68 (3): 1938-1958 被引量:47
标识
DOI:10.1287/mnsc.2021.4011
摘要

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.
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