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

Gao, Xiangyu

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https://hdl.handle.net/2142/98234 Copy

Description

Title

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

Author(s)

Gao, Xiangyu

Issue Date

2017-06-26

Director of Research (if dissertation) or Advisor (if thesis)

Chen, Xin

Doctoral Committee Chair(s)

Chen, Xin

Committee Member(s)

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

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Industrial Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

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

Abstract

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.

Graduation Semester

2017-08

Type of Resource

text

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

Copyright and License Information

Copyright 2017 Xiangyu Gao

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