Bounitsis, Georgios L;
Papageorgiou, Lazaros G;
Charitopoulos, Vassilis M;
(2022)
Data-driven scenario generation for two-stage stochastic programming.
Chemical Engineering Research and Design
, 187
pp. 206-224.
10.1016/j.cherd.2022.08.014.
(In press).
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Abstract
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 integration 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 highlight the benefits regarding the quality of the generated scenario trees by evaluating the corresponding obtained stochastic solutions.
| Type: | Article |
|---|---|
| Title: | Data-driven scenario generation for two-stage stochastic programming |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.cherd.2022.08.014 |
| Publisher version: | https://doi.org/10.1016/j.cherd.2022.08.014 |
| Language: | English |
| Additional information: | © 2022 Published by Elsevier Ltd. This is an open access article under the CC BY 4.0 license Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/) |
| Keywords: | Scenario generation, Stochastic programming, Data-driven optimisation, Moment Matching Problem, Copulas |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10155902 |
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