A maximum entropy approach to the newsvendor problem with partial information
Journal article, Peer reviewed

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Date
2013Metadata
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Original version
European Journal of Operational Research 2013, 228(1):190-200 10.1016/j.ejor.2013.01.031Abstract
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
Description
“NOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research 2013, 228(1):190-200,doi:10.1016/j.ejor.2013.01.031¨ Copyright © 2013 Elsevier B.V. All rights reserved