Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning

• 成果类型: Article
• 署名作者: Chen, Boxiao; Chao, Xiuli; Ahn, Hyun-Soo
• 署名单位: University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2018.1808
• 发表日期: 2019
• 页码: 1035-1052
• 关键词: Newsvendor problem; BANDIT

摘要

We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price sensitive, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory replenishment quantity with the objective of maximizing total profit, but it knows neither the average demand (as a function of price) nor the distribution of demand uncertainty a priori; hence, it has to make pricing and ordering decisions based on observed demand data. We propose a nonparametric, data-driven algorithm that learns about the demand on the fly and, concurrently, applies learned information to make replenishment and pricing decisions. The algorithm integrates learning and action in a sense that the firm actively experiments on pricing and inventory levels to collect demand information with minimum profit loss. Besides convergence of optimal policies, we show that the regret of the algorithm, defined as the average profit loss compared with that of the optimal solution had the firm known the underlying demand information, vanishes at the fastest possible rate as the planning horizon increases.