Empirical comparison of robust, data driven and stochastic optimization
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311861975-MIT.pdf
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Full printable version
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2.67 MB
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Author(s)
Wang, Yanbo, S.M. Massachusetts Institute of Technology
Advisor(s)
Dimitris J. Bertsimas.
Date Issued
2008
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we compare computationally four methods for solving optimization problems under uncertainty: * Robust Optimization (RO) * Adaptive Robust Optimization (ARO) * Data Driven Optimization (DDO) * stochastic Programming (SP) We have implemented several computation experiments to demonstrate the different performance of these methods. We conclude that ARO outperform RO, which has a comparable performance with DDO. SP has a comparable performance with RO when the assumed distribution is the same as the true underlying distribution, but under performs RO when the assumed distribution is different from the true distribution.
Description
Includes bibliographical references (leaf 49).
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.
Subjects
Computation for Design and Optimization Program.
MIT Department
Massachusetts Institute of Technology. Computation for Design and Optimization Program
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