Advanced Search
DSpace@MIT

The Big Data Newsvendor: Practical Insights from Machine Learning

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.author Rudin, Cynthia
dc.contributor.author Vahn, Gah-Yi
dc.date.accessioned 2014-03-14T20:08:09Z
dc.date.available 2014-03-14T20:08:09Z
dc.date.issued 2014-02-06
dc.identifier.uri http://hdl.handle.net/1721.1/85658
dc.description This is a revision of previously published DSpace entry: http://hdl.handle.net/1721.1/81412. en_US
dc.description.abstract We investigate the newsvendor problem when one has n observations of p features related to the demand as well as past demands. Both small data (p=n = o(1)) and big data (p=n = O(1)) are considered. For both cases, we propose a machine learning algorithm to solve the problem and derive a tight generalization bound on the expected out-of-sample cost. The algorithms can be extended intuitively to other situations, such as having censored demand data, ordering for multiple, similar items and having a new item with limited data. We show analytically that our custom-designed, feature-based approach can be better than other data-driven approaches such as Sample Average Approximation (SAA) and separated estimation and optimization (SEO). Our method can also naturally incorporate the operational statistics method. We then apply the algorithms to nurse staffing in a hospital emergency room and show that (i) they can reduce the median out-of-sample cost by up to 46% and 16% compared to SAA and SEO respectively, with statistical significance at 0.01, and (ii) this is achieved either by carefully selecting a small number of features and applying the small data algorithm, or by using a large number of features and using the big data algorithm, which automates feature-selection. en_US
dc.language.iso en_US en_US
dc.publisher DSpace en_US
dc.relation.ispartofseries MIT Sloan Working Paper Series;5032-13
dc.relation.replaces http://hdl.handle.net/1721.1/81412
dc.relation.uri http://hdl.handle.net/1721.1/81412
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject big data en_US
dc.subject newsvendor en_US
dc.subject machine learning en_US
dc.subject Sample Average Approximation en_US
dc.subject statistical learning en_US
dc.subject theory en_US
dc.subject quantile regression en_US
dc.title The Big Data Newsvendor: Practical Insights from Machine Learning en_US
dc.type Working Paper en_US


Files in this item

Name Size Format Description
RV_BigDataNV_onli ... 1.552Mb PDF

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States
MIT-Mirage