Restricted-recourse bounds for stochastic linear programming

We consider the problem of bounding the expected value of a linear program (LP) containing random coefficients, with applications to solving two-stage stochastic programs. An upper bound for minimizations is derived from a re striction of an equivalent, penalty-based formulation of the primal stochas tic LP, and a lower bound is obtained from a restriction of a reformulation of the dual. Our "restricted-recourse bounds" are more general and more ea sily computed than most other bounds because random coefficients may appear anywhere in the LP, neither independence nor boundedness of the coefficien ts is needed, and the bound is computed by solving a single LP or nonlinear program. Analytical examples demonstrate that the new bounds can be strong er than complementary Jensen bounds. (An upper bound is "complementary" to a lower bound, and vice versa). In computational work, we apply the bounds to a two-stage stochastic program for semiconductor manufacturing with unce rtain demand and production rates.