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A Tractable Approximation of Stochastic Programming via Robust Optimization

Melvyn Sim (dscsimm***at***nus.edu.sg)
Xin Chen (xinchen***at***uiuc.edu)
Jiawei Zhang (jzhang***at***stern.nyu.edu)
Sun Peng (peng.sun***at***duke.edu)

Abstract: Stochastic programming, despite its immense modeling capabilities, is well known to be computationally excruciating. In this paper, we introduce a unified framework of approximating multiperiod stochastic programming from the perspective of robust optimization. Specifically, we propose a framework that integrates multistage modeling with safeguarding constraints. The framework is computationally tractable in the form of second order cone programming (SOCP) and scalable across periods. We compare the computational performance of our proposal with classical stochastic programming approach using sampling approximations and report very encouraging results for a class of project management problems.

Keywords: Robust Optimization, Stochastic Programming, Chance Constraints, Second Order Cone Programming

Category 1: Robust Optimization

Category 2: Stochastic Programming

Category 3: Linear, Cone and Semidefinite Programming (Second-Order Cone Programming )

Citation: Working Paper, NUS Business School

Download: [PDF]

Entry Submitted: 02/09/2006
Entry Accepted: 02/10/2006
Entry Last Modified: 03/06/2006

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