Data-driven scenario generation for two-stage stochastic programming

Optimisation under uncertainty has always been a focal point within the Process Systems Engineering (PSE) research agenda. In particular, the efficient manipulation of large amount of data for the uncertain parameters constitutes a crucial condition for effectively tackling stochastic programming problems. In this context, this work proposes a new data-driven Mixed-Integer Linear Programming (MILP) model for the Distribution & Moment Matching Problem (DMP). For cases with multiple uncertain parameters a copula -based simulation of initial scenarios is employed as preliminary step. Moreover, the in-tegration of clustering methods and DMP in the proposed model is shown to enhance computational performance. Finally, we compare the proposed approach with state-of-the-art scenario generation methodologies. Through a number of case studies we high-light the benefits regarding the quality of the generated scenario trees by evaluating the corresponding obtained stochastic solutions.(c) 2022 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://creative-commons.org/licenses/by/4.0/).

Automated summary

Optimisation under uncertainty is a focal point in Process Systems Engineering research. This study proposes a data-driven Mixed-Integer Linear Programming model for handling large amount of data for uncertain parameters in solving stochastic programming problems. The proposed approach is shown to have advantages in terms of the quality of generated scenario trees compared to state-of-the-art scenario generation methodologies.