Learning Newsvendor Problems With Intertemporal Dependence and Moderate Non-stationarities

• 成果类型: Article
• 署名作者: Qi, Meng; Shen, Zuo-Jun(Max); Zheng, Zeyu
• 署名单位: Cornell University; University of Hong Kong; University of Hong Kong; University of California System; University of California Berkeley
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478241242122
• 发表日期: 2024
• 页码: 1196-1213
• 关键词: Newsvendor problem; intertemporal dependent data; non-stationarity; generalization bound

摘要

This work provides performance guarantees for solving data-driven contextual newsvendor problems when the contextual data contains intertemporal dependence and non-stationarities. While machine learning tools have observed increasing use in data-driven inventory management problems, most of the existing work assumes that the contextual data are independent and identically distributed (often referred to as i.i.d.). However, such assumptions are often violated in real operational environments where the contextual data are sequentially generated with intertemporal correlations and possible non-stationarities. By accommodating these naturally arising operational environments, our work adopts comparatively more realistic assumptions and develops out-of-sample performance bounds for learning data-driven contextual newsvendor problems.