File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Newsvendor-type models with decision-dependent uncertainty

Author(s)
Lee, SoonhuiHomem-de-Mello, TitoKleywegt, Anton J.
Issued Date
2012-10
DOI
10.1007/s00186-012-0396-3
URI
https://scholarworks.unist.ac.kr/handle/201301/11242
Fulltext
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84867278770
Citation
MATHEMATICAL METHODS OF OPERATIONS RESEARCH, v.76, no.2, pp.189 - 221
Abstract
Models for decision-making under uncertainty use probability distributions to represent variables whose values are unknown when the decisions are to be made. Often the distributions are estimated with observed data. Sometimes these variables depend on the decisions but the dependence is ignored in the decision maker's model, that is, the decision makermodels these variables as having an exogenous probability distribution independent of the decisions, whereas the probability distribution of the variables actually depend on the decisions. It has been shown in the context of revenue management problems that such modeling error can lead to systematic deterioration of decisions as the decision maker attempts to refine the estimates with observed data. Many questions remain to be addressed. Motivated by the revenue management, newsvendor, and a number of other problems, we consider a setting in which the optimal decision for the decision maker's model is given by a particular quantile of the estimated distribution, and the empirical distribution is used as estimator. We give conditions under which the estimation and control process converges, and showthat although in the limit the decision maker's model appears to be consistent with the observed data, the modeling error can cause the limit decisions to be arbitrarily bad.
Publisher
SPRINGER HEIDELBERG
ISSN
1432-2994
Keyword (Author)
Newsvendor modelData-driven optimizationStochasticapproximation
Keyword
CONVERGENCEDEMAND

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.