We introduce a method to improve the tractability of the well-known Sample Average Approximation (SAA) without compromising important theoretical properties, such as convergence in probability and the consistency of an independent and identically distributed (iid) sample. We consider each scenario as a polyhedron of the mix of first-stage and second-stage decision variables. According to John's theorem, the Lowner-John ellipsoid of each polyhedron will be unique which means that different scenarios will have correspondingly different Lowner-John ellipsoids. By optimizing the objective function regarding both feasible regions of the polyhedron and its unique Lowner-John ellipsoid, respectively, we obtain a pair of optimal values, which would be a coordinate on a two-dimensional plane. The scenarios, whose coordinates are close enough on the plane, will be treated as one scenario; thus our method reduces the sample size of an iid sample considerably. Instead of using a large iid sample d 查看全文>>