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A Robust Optimization Perspective of Stochastic Programming

Melvyn Sim (dscsimm***at***nus.edu.sg)
Xin Chen (xinchen***at***uiuc.edu)
Peng Sun (psun***at***duke.edu)

Abstract: In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for bounded random variables known as the forward and backward deviations. These deviation measures capture distributional asymmetry and lead to better approximations of chance constraints. We also propose a tractable robust optimization approach for obtaining robust solutions to a class of stochastic linear optimization problems where the risk of infeasibility can be tolerated as a tradeoff to improve upon the objective value. An attractive feature of the framework is the computational scalability to multiperiod models. We show an application of the framework for solving a project management problem with uncertain activity completion time.

Keywords: Robust Optimization, Stochastic Programming, Deviation Measures

Category 1: Robust Optimization

Category 2: Stochastic Programming

Citation: Working paper, NUS Business school, 2005

Download: [PDF]

Entry Submitted: 06/06/2005
Entry Accepted: 06/18/2005
Entry Last Modified: 06/06/2005

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