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 also equivalent to a second-order cone optimization problem without the linear decision rule. A numerical example is presented to demonstrate the convenience and computational advantage of this approach.
About the speaker: Professor Jie Sun obtained his MSc from the Chinese Academy of Science in 1981 and PhD from the University of Washington in 1986, respectively. He has been assistant professor in Northwestern University (Evanston) and associate professor, professor, and chair professor in 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 a winner of the Outstanding University Researcher Award at National University of Singapore. Currently, he is the chair of Pacific Optimization Research Activity Group and editorial board members of Mathematics of Operations Research, Pacific Journal of Optimization, Optimization Methods and Software, etc.