Bounitsis, Georgios L; Papageorgiou, Lazaros G; Charitopoulos, Vassilis M; (2022) Data-Driven Scenario Generation for Two-Stage Stochastic Programming. In: Yamashita, Yoshiyuki and Kano, Manabu, (eds.) Computer Aided Chemical Engineering. (pp. 1231-1236). Elsevier: Amsterdam, The Netherlands.

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Abstract

The efficient exploitation of large amount of data for the uncertain parameters constitutes a crucial condition for effectively handling stochastic programming problems. In this work we propose a novel data-driven mixed-integer linear programming (MILP) model for the Distribution Matching Problem (DMP). Ιn cases of multiple uncertain parameters, sampling using copulas is conducted as preliminary step. The integration of clustering methods and DMP in the proposed model is proven to improve the computational efficiency. For the evaluation of the performance of the proposed scenario generation approaches several case studies of a two-stage stochastic programming problem are examined. Compared with state-of-the-art scenario generation (SG) approaches the proposed model is shown to achieve consistently the lowest errors regarding the expected values when compared to full-space stochastic solutions as well as manages to preserve good accuracy in the resulting probabilistic and statistical qualities of the reduced generated sets.

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