Data-driven dynamic optimization with auxiliary covariates

Optimization under uncertainty forms the foundation for many of the fundamental problems the operations research community seeks to solve. In this thesis, we develop and analyze algorithms that incorporate ideas from machine learning to optimize uncertain objectives directly from data. In the first chapter, we consider problems in which the decision affects the observed outcome, such as in personalized medicine and pricing. We present a framework for using observational data to learn to optimize an uncertain objective over a continuous and multi-dimensional decision space. Our approach accounts for the uncertainty in predictions, and we provide theoretical results that show this adds value. In addition, we test our approach on a Warfarin dosing example, and it outperforms the leading alternative methods.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• McCord, Christopher George.

Advisor dc:contributor.advisor

• Dimitris Bertsimas.

• Operations Research Center.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122098

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122098