A Multistage Stochastic Programming Approach with Strategies for Uncertainty Reduction in the Planning of Process Networks with Uncertain Yields

In this paper we consider multistage stochastic programs with endogenous parameters where investment strategies are considered to reduce uncertainty, and time-varying distributions are used to describe uncertainty. We present the proposed ideas in the context of the planning of process networks with uncertain yields. We propose a new mixed-integer/disjunctive programming model which is reformulated as a mixed-integer linear program. The model can be solved through an LP-based branch and bound for smaller instances or with a duality-based branch and bound for larger problems.