Online and offline learning in operations

With the rapid advancement of information technology and accelerated development of data science, the importance of integrating data into decision-making has never been stronger. In this thesis, we propose data-driven algorithms to incorporate learning from data in three operations problems, concerning both online learning and offline learning settings. First, we study a single product pricing problem with demand censoring in an offline data-driven setting. In this problem, a retailer is given a finite level of inventory, and faces a random demand that is price sensitive in a linear fashion with unknown parameters and distribution. Any unsatisfied demand is lost and unobservable. The retailer's objective is to use offline censored demand data to find an optimal price, maximizing her expected revenue with finite inventories.

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

2020

Author dc:creator

• Wang, Li

Advisor dc:contributor.advisor

• David Simchi-Levi.

• Operations Research Center.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

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/1721.1/129080

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129080