Two-stage stochastic linear programming is a classical model in operations research. We study this model, but only assume the availability of the first and second order moment information of the random variables. By using duality of semi-infinite programming and adopting a linear decision rule, we show that a deterministic equivalence of the two-stage problem can be reformulated as a second-order cone optimization problem. If information on the extreme points of the dual polyhedron of the recourse problem is known, then the two-stage problem is equivalent to a second-order cone optimization problem without the linear decision rule. A simplified production example is presented to demonstrate the application and computational advantage of this approach.
Professor Jie Sun obtained his MSc from the Chines Academy of Science in 1981 and PhD at the University of Washington in 1986, respectively. He has been an Assistant Professor at Northwestern University (Evanston) and Associate Professor, Professor, and Chaired Professor at the National University of Singapore. His research focuses on theory, applications, and algorithms of optimization. He has published more than 100 research papers in professional journals such as Operations Research, Mathematical Programming, and SIAM Journal on Optimization. He was an award winner of the Outstanding University Researcher Award at the National University of Singapore. Currently, he is the Chair of the Pacific Optimization Research Activity Group and serves as an Editorial Board Member of several significant optimization journals such as Mathematics of Operations Research, Pacific Journal of Optimization, and Optimization Methods and Software.