A maximum entropy approach to the newsvendor problem with partial information

Jonas Andersson, Kurt Jörnsten (), Sigrid Lise Nonås (), Leif K. Sandal () and Jan Ubøe ()
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Kurt Jörnsten: Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration, Postal: NHH , Department of Finance and Management Science, Helleveien 30, N-5045 Bergen, Norway, http://www.nhh.no/Default.aspx?ID=2019
Sigrid Lise Nonås: Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration, Postal: NHH , Department of Finance and Management Science, Helleveien 30, N-5045 Bergen, Norway, http://www.nhh.no/Default.aspx?ID=1985
Leif K. Sandal: Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration, Postal: NHH , Department of Finance and Management Science, Helleveien 30, N-5045 Bergen, Norway, http://www.nhh.no/Default.aspx?ID=1989
Jan Ubøe: Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration, Postal: NHH , Department of Finance and Management Science, Helleveien 30, N-5045 Bergen, Norway, http://www.nhh.no/Default.aspx?ID=2025

No 2011/14, Discussion Papers from Department of Finance and Management Science, Norwegian School of Economics

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)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2011-08-31
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