The edge of large-scale optimization in transportation and machine learning

This thesis focuses on impactful applications of large-scale optimization in transportation and machine learning. Using both theory and computational experiments, we introduce novel optimization algorithms to overcome the tractability issues that arise in real world applications. We work towards the implementation of these algorithms, through software contributions, public policy work, and a formal study of machine learning interpretability. Our implementation in Boston Public Schools generates millions of dollars in yearly transportation savings and led to important public policy consequences in the United States. This work is motivated by large-scale transportation problems that present significant optimization challenges. In particular, we study the problem of ride-sharing, the online routing of hundreds of thousands of customers every day in New York City.

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

• Martin, Sébastien

Advisor dc:contributor.advisor

• Patrick Jaillet and 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/1721.1/122388

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122388