A multistage stochastic programming approach with strategies for uncertainty reduction in the synthesis of process networks with uncertain yields

We consider in this paper the synthesis of process networks with time-varying uncertain yields in which investment in pilot plants can be considered to reduce uncertainty of the yields. We formulate this problem as a multistage stochastic program with decision dependent elements where investment strategies are considered to reduce uncertainty, and time-varying distributions are used to describe uncertainty. We propose a new mixed-integer/disjunctive programming model which is reformulated as a mixed-integer linear program. Since the model can only be solved through an LP-based branch and bound for smaller instances, we propose a duality-based branch and bound algorithm for solving larger problems. Two numerical examples are presented to illustrate the application of the proposed method.