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. (C) 2007 Elsevier Ltd. All rights reserved.
stochastic programming branch and bound design under uncertainty lagrangean decomposition