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A maximum entropy approach to the newsvendor problem with partial information

Andersson, Jonas; Jörnsten, Kurt; Nonås, Sigrid Lise; Sandal, Leif Kristoffer; Ubøe, Jan
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URI
http://hdl.handle.net/11250/164175
Date
2011-08
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  • Discussion papers (FOR) [514]
Abstract
In this paper, we consider the newsvendor model under partial information, i.e., where

the demand distribution D is partly unknown. We focus on the classical case where the

retailer only knows the expectation and variance of D. The standard approach is then to

determine the order quantity using conservative rules such as minimax regret or Scarf's rule.

We compute instead the most likely demand distribution in the sense of maximum entropy.

We then compare the performance of the maximum entropy approach with minimax regret

and Scarf's rule on large samples of randomly drawn demand distributions. We show that

the average performance of the maximum entropy approach is considerably better than either

alternative, and more surprisingly, that it is in most cases a better hedge against bad results.
Publisher
Norwegian School of Economics. Department of Finance and Management Science
Series
Discussion paper;2011:14

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