The Big Data Newsvendor: Practical Insights from Machine Learning
Name
RV_BigDataNV_online_6Feb14.pdf
Size
1.55 MB
Format
Adobe PDF
Checksum (MD5)
c5c33ff38889653ec33bd02c84749e0b
Author(s)
Rudin, Cynthia
Vahn, Gah-Yi
Date Issued
February 6, 2014
Publisher
DSpace
Series/Report no.
MIT Sloan Working Paper Series;5032-13
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.
Description
This is a revision of previously published DSpace entry: http://hdl.handle.net/1721.1/81412.
Subjects
big data
newsvendor
machine learning
Sample Average Approximation
statistical learning
theory
quantile regression
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
Persistent DSpace Link
