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 pr...

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Vydáno v:Chemical engineering research & design Ročník 187; s. 206 - 224
Hlavní autoři: Bounitsis, Georgios L., Papageorgiou, Lazaros G., Charitopoulos, Vassilis M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.11.2022
Témata:
Copulas
Data-driven optimisation
Moment Matching Problem
Scenario generation
Stochastic programming
Copulas
Moment Matching Problem
Data-driven optimisation
Scenario generation
Stochastic programming
ISSN:0263-8762
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Shrnutí: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. •A novel scenario generation method hybridising mathematical programming and copula-based sampling.•Moment & distribution matching problem are modelled as a Mixed Integer Linear Programming optimisation problem.•Case studies of the PSE literature indicate the benefits of the proposed method over state-of-the-art approaches.
ISSN:0263-8762
DOI:10.1016/j.cherd.2022.08.014