Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models

• 成果类型: Article
• 署名作者: Qin, Hanzhang; Simchi-Levi, David; Wang, Li
• 署名单位: Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4212
• 发表日期: 2022
• 页码: 6591-6609
• 关键词: Dynamic pricing; INVENTORY CONTROL; revenue management; approximation algorithm; Data-Driven Optimization; dynamic programming

摘要

We study the classic multiperiod joint pricing and inventory control problem in a data-driven setting. In this problem, a retailermakes periodic decisions on the prices and inventory levels of a product that she wishes to sell. The retailer's objective is to maximize the expected profit over a finite horizon bymatching the inventory level with a randomdemand, which depends on the price in each period. In reality, the demand functions or random noise distributions are usually difficult to know exactly, whereas past demand data are relatively easy to collect. We propose a data-driven approximation algorithm that uses precollected demand data to solve the joint pricing and inventory control problem. We assume that the retailer does not know the noise distributions or the true demand functions; instead, we assume either she has access to demand hypothesis sets and the true demand functions can be represented by nonnegative combinations of candidate functions in the demand hypothesis sets, or the true demand function is generalized linear. We prove the algorithm's sample complexity bound: the number of data samples needed in each period to guarantee a near-optimal profit is O(T-6 is an element of(-2)logT), where T is the number of periods, and is an element of is the absolute difference between the expected profit of the data-driven policy and the expected optimal profit. In a numerical study, we demonstrate the construction of demand hypothesis sets from data and showthat the proposed data-driven algorithmsolves the dynamic problem effectively and significantly improves the optimality gaps over the baseline algorithms.