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Title:Stochastic optimization with decisions truncated by random variables and its applications in operations
Author(s):Gao, Xiangyu
Director of Research:Chen, Xin
Doctoral Committee Chair(s):Chen, Xin
Doctoral Committee Member(s):Lim, Michael; Wang, Qiong; Xin, Linwei
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Industrial Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(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.
Issue Date:2017-06-26
Type:Thesis
URI:http://hdl.handle.net/2142/98234
Rights Information:Copyright 2017 Xiangyu Gao
Date Available in IDEALS:2017-09-29
Date Deposited:2017-08


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