Stochastic optimization with decisions truncated by random variables and its applications in operations

We study stochastic optimization problems with decisions truncated by random variables and its applications in operations management. The technical difficulty of these problems is that the optimization problem is not convex due to the truncation. We develop a transformation technique to convert the original non-convex optimization problems to convex ones while preservation some desired structural properties, which are useful for characterizing optimal decision policies and conducting comparative statics. Our transformation technique provides a unified approach to analyze a broad class of models in inventory control and revenue management. In additional, we develop efficient algorithms to solve the transformed stochastic optimization problem.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Gao, Xiangyu

Contributors dc:contributor

• Chen, Xin
• Lim, Michael
• Wang, Qiong
• Xin, Linwei

• Stochastic optimization
• Convexity
• Supply capacity uncertainty
• Revenue management

Statement dc:rights

• Copyright 2017 Xiangyu Gao

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/98234

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/98234