A Robust Optimization Perspective on Stochastic Programming

Xin Chen (), Melvyn Sim () and Peng Sun ()
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Xin Chen: Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
Melvyn Sim: NUS Business School, National University of Singapore and Singapore MIT Alliance (SMA), Singapore
Peng Sun: Fuqua School of Business, Duke University, Durham, North Carolina 27708

Operations Research, 2007, vol. 55, issue 6, 1058-1071

Abstract: In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for random variables termed the forward and backward deviations . These deviation measures capture distributional asymmetry and lead to better approximations of chance constraints. Using a linear decision rule, we also propose a tractable approximation approach for solving a class of multistage chance-constrained stochastic linear optimization problems. An attractive feature of the framework is that we convert the original model into a second-order cone program, which is computationally tractable both in theory and in practice. We demonstrate the framework through an application of a project management problem with uncertain activity completion time.

Keywords: programming; stochastic (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (98)

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