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A maximum entropy approach to the newsvendor problem with partial information

Jonas Andersson, Kurt Jörnsten, Sigrid Lise Nonås, Leif Sandal () and Jan Ubøe ()

European Journal of Operational Research, 2013, vol. 228, issue 1, 190-200

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.

Keywords: Newsvendor model; Entropy; Partial information (search for similar items in EconPapers)
Date: 2013
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Working Paper: A maximum entropy approach to the newsvendor problem with partial information (2011) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:228:y:2013:i:1:p:190-200

DOI: 10.1016/j.ejor.2013.01.031

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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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