A maximum entropy approach to the newsvendor problem with partial information
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.
|Date of creation:||31 Aug 2011|
|Date of revision:|
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- Yingjie Lan & Michael O. Ball & Itir Z. Karaesmen, 2011. "Regret in Overbooking and Fare-Class Allocation for Single Leg," Manufacturing & Service Operations Management, INFORMS, vol. 13(2), pages 194-208, December.
- Lee, Chih-Ming & Hsu, Shu-Lu, 2011. "The effect of advertising on the distribution-free newsboy problem," International Journal of Production Economics, Elsevier, vol. 129(1), pages 217-224, January.
- Sandal, Leif K. & Ubøe, Jan, 2012. "Stackelberg equilibria in a multiperiod vertical contracting model with uncertain and price-dependent demand," Discussion Papers 2012/2, Department of Business and Management Science, Norwegian School of Economics.
- Jörnsten, Kurt & Lise Nonås, Sigrid & Sandal, Leif & Ubøe, Jan, 2012. "Transfer of risk in the newsvendor model with discrete demand," Omega, Elsevier, vol. 40(3), pages 404-414.
- Georgia Perakis & Guillaume Roels, 2010. "Robust Controls for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 12(1), pages 56-76, November.
- Lin, Jun & Ng, Tsan Sheng, 2011. "Robust multi-market newsvendor models with interval demand data," European Journal of Operational Research, Elsevier, vol. 212(2), pages 361-373, July.
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