The Data-Driven Newsvendor Problem: New Bounds and Insights

• 成果类型: Article
• 署名作者: Levi, Retsef; Perakis, Georgia; Uichanco, Joline
• 署名单位: Massachusetts Institute of Technology (MIT); University of Michigan System; University of Michigan
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2015.1422
• 发表日期: 2015
• 页码: 1294-1306
• 关键词: INVENTORY; optimization; demand

摘要

Consider the newsvendor model, but under the assumption that the underlying demand distribution is not known as part of the input. Instead, the only information available is a random, independent sample drawn from the demand distribution. This paper analyzes the sample average approximation (SAA) approach for the data-driven newsvendor problem. We obtain a new analytical bound on the probability that the relative regret of the SAA solution exceeds a threshold. This bound is significantly tighter than existing bounds, and it matches the empirical accuracy of the SAA solution observed in extensive computational experiments. This bound reveals that the demand distribution's weighted mean spread affects the accuracy of the SAA heuristic.