A Tractable Approximation of Stochastic Programming via Robust Optimization

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. ∗Department of Mechanical and Industrial Engineering, University of Illinois at Urbana-Champaign. Email: [email protected] †NUS Business School, National University of Singapore and Singapore-MIT-Alliance (SMA). Email: [email protected]. The research of the author is supported by SMA and NUS academic research grants R-314-000-066-122 and R-314-000-068-112 ‡Fuqua School of Business, Duke University Box 90120, Durham, NC 27708, USA. Email: [email protected] §Stern School of Business, New York University, New York, NY, 10012. Email: [email protected]