Regret in the newsvendor model with partial information. (English) Zbl 1167.90350

Perakis, Georgia; Roels, Guillaume

Oper. Res. 56, No. 1, 188-203 (2008).

Summary: Traditional stochastic inventory models assume full knowledge of the demand probability distribution. However, in practice, it is often difficult to completely characterize the demand distribution, especially in fast-changing markets. In this paper, we study the newsvendor problem with partial information about the demand distribution (e.g., mean, variance, symmetry, unimodality). In particular, we derive the order quantities that minimize the newsvendor’s maximum regret of not acting optimally. Most of our solutions are tractable, which makes them attractive for practical application. Our analysis also generates insights into the choice of the demand distribution as an input to the newsvendor model. In particular, the distributions that maximize the entropy perform well under the regret criterion. Our approach can be extended to a variety of problems that require a robust but not conservative solution.

MSC:

90B05	Inventory, storage, reservoirs
90C31	Sensitivity, stability, parametric optimization
90C34	Semi-infinite programming