Empirical comparison of robust, data driven and stochastic optimization
Author(s)Wang, Yanbo, S.M. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Computation for Design and Optimization Program.
Dimitris J. Bertsimas.
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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.
Includes bibliographical references (leaf 49).Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.
DepartmentMassachusetts Institute of Technology. Computation for Design and Optimization Program.
Massachusetts Institute of Technology
Computation for Design and Optimization Program.