Measures of information in multistage stochastic programming

Abstract

Multistage stochastic programs, which involve sequences of decisions over time, are usually hard to solve in realistically sized problems. Providing bounds for their optimal solution may help in evaluating whether it is worth the additional computations for the stochastic program versus simplified approaches. In this paper we present a summary of the results in [22] where we generalize the value of information gained from deterministic, pair solution and rolling-horizon approximation in the two-stage case to the multistage stochastic formulation. Numerical results on a case study related to a simple transportation problem illustrate the described relationships.