Stochastic programming problems with generalized integrated chance constraints

Abstract

We propose an alternative formulation of stochastic programs using penalty functions. The expectations of penalties can be left as constraints leading to generalized integrated chance constraints, or incorporated into the objective as a penalty term.

We show that the penalty problems are asymptotically equivalent under quite mild conditions. We discuss applications of sample-approximation techniques to the problems with generalized integrated chance constraints and propose rates of convergence for the set of feasible solutions.

Keywords

• chance constraints
• integrated chance constraints
• penalty functions
• sample approximations
• blending problem