Please use this identifier to cite or link to this item: https://doi.org/10.1287/opre.1070.0441
Title: A robust optimization perspective on stochastic programming
Authors: Chen, X.
Sim, M. 
Sun, P.
Keywords: Programming: stochastic
Issue Date: 2007
Source: Chen, X., Sim, M., Sun, P. (2007). A robust optimization perspective on stochastic programming. Operations Research 55 (6) : 1058-1071. ScholarBank@NUS Repository. https://doi.org/10.1287/opre.1070.0441
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. © 2007 INFORMS.
Source Title: Operations Research
URI: http://scholarbank.nus.edu.sg/handle/10635/44052
ISSN: 0030364X
DOI: 10.1287/opre.1070.0441
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