Two-Stage Stochastic Mixed-Integer Linear Programming: From Scenarios to Conditional Scenarios

This paper is a second part of [2] where the conditional scenario (CS) concept was introduced and applied to approximate the multinormal distribution in the context of two-stage stochastic mixed-integer linear programming. Now we propose an improved definition of conditional scenario which is suitable to approximate any distribution (not only the multinormal one). In particular, it allows to approximate a potentially large set of scenarios by a small set of conditional scenarios. In the computational study here presented, the CS approach has outperformed the scenario reduction approach based on probability distances, in terms of solution time.