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Newsvendor Problems with Demand Shocks and Unknown Demand Distributions
Author(s) -
O'Neil Shawn,
Zhao Xuying,
Sun Daewon,
Wei Jerry C.
Publication year - 2016
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12187
Subject(s) - newsvendor model , demand forecasting , market demand schedule , demand patterns , demand shock , computer science , on demand , demand management , distribution (mathematics) , dynamic demand , economics , speculative demand , aggregate demand , microeconomics , supply chain , operations management , business , mathematics , marketing , power (physics) , macroeconomics , mathematical analysis , quantum mechanics , multimedia , physics , monetary economics , monetary policy
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