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.2014.04.012
Abstract or Description
This paper brings systematic methods for scenario tree generation to the attention of the Process Systems Engineering community. We focus on a general, data-driven optimization-based method for generating scenario trees that does not require strict assumptions on the probability distributions of the uncertain parameters. Using as a basis the Moment Matching Problem (MMP), originally proposed by Høyland and Wallace (2001), we propose matching marginal (Empirical) Cumulative Distribution Function information of the uncertain parameters in order to cope with potentially under-specified MMP formulations. The new method gives rise to a Distribution Matching Problem (DMP) that is aided by predictive analytics. We present two approaches for generating multi-stage scenario trees by considering time series modeling and forecasting. The aforementioned techniques are illustrated with a production planning problem with uncertainty in production yield and correlated product demands.
Computers and Chemical Engineering, 68, 7-23.