Newsvendor Problems with Demand Shocks and Unknown Demand Distributions

作     者：O'Neil, Shawn Zhao, Xuying Sun, Daewon Wei, Jerry C. 

作者机构：Oregon State Univ Ctr Genome Res & Biocomp ALS 3135 Corvallis OR 97331 USA Univ Notre Dame Dept Management Mendoza Coll Business Notre Dame IN 46556 USA 

出 版 物：《DECISION SCIENCES》 (决策科学)

年 卷 期：2016年第47卷第1期

页      面：125-156页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

主　　题：Demand Forecasting Demand Shocks Inventory Management Machine Learning Algorithm Newsvendor Problem and Stochastic Demands 

摘      要：In today s competitive market, demand volume and even the underlying demand distribution can change quickly for a newsvendor seller. We refer to sudden changes in demand distribution as demand shocks. When a newsvendor seller has limited demand distribution information and also experiences underlying demand shocks, the majority of existing methods for newsvendor problems may not work well since they either require demand distribution information or assume stationary demand distribution. We present a new, robust, and effective machine learning algorithm for newsvendor problems with demand shocks but without any demand distribution information. The algorithm needs only an approximate estimate of the lower and upper bounds of demand range;no other knowledge such as demand mean, variance, or distribution type is necessary. We establish the theoretical bounds that determine this machine learning algorithm s performance in handling demand shocks. Computational experiments show that this algorithm outperforms the traditional approaches in a variety of situations including large and frequent shocks of the demand mean. The method can also be used as a meta-algorithm by incorporating other traditional approaches as experts. Working together, the original algorithm and the extended meta-algorithm can help manufacturers and retailers better adapt their production and inventory control decisions in dynamic environments where demand information is limited and demand shocks are frequent