Date of Original Version
This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version is available at http://dx.doi.org/10.1016/j.compchemeng.2010.11.013
Abstract or Description
In this paper, we present a generic mixed-integer linear multistage stochastic programming (MSSP) model considering endogenous uncertainty in some of the parameters. To address the issue that the number of non-anticipativity (NA) constraints increases exponentially with the number of uncertain parameters and/or its realizations, we present a new theoretical property that significantly reduces the problem size and complements two previous properties. Since one might generate reduced models that are still too large to be solved directly, we also propose three solution strategies: a k-stage constraint strategy where we only include the NA constraints up to a specified number of stages, an iterative NAC relaxation strategy, and a Lagrangean decomposition algorithm that decomposes the problem into scenarios. Numerical results for two process network examples are presented to illustrate that the proposed solution strategies yield significant computational savings.
Computers and Chemical Engineering, 36, 11, 2235-2247.