Bounitsis, Georgios L; Papageorgiou, Lazaros G; Charitopoulos, Vassilis M; (2024) Stable optimisation-based scenario generation via game theoretic approach. Computers and Chemical Engineering , 185 , Article 108646. 10.1016/j.compchemeng.2024.108646.

Text DMPNASH_CACE_Minor_Unmarked.pdf - Other Access restricted to UCL open access staff until 16 March 2025. Download (4MB)

Abstract

Systematic scenario generation (SG) methods have emerged as an invaluable tool to handle uncertainty towards the efficient solution of stochastic programming (SP) problems. The quality of SG methods depends on their consistency to generate scenario sets which guarantee stability on solving SPs and lead to stochastic solutions of good quality. In this context, we delve into the optimisation-based Distribution and Moment Matching Problem (DMP) for scenario generation and propose a game theoretic approach which is formulated as a Mixed-Integer Linear Programming (MILP) model. Nash bargaining approach is employed and the terms of the objective function regarding the statistical matching of the DMP are considered as players. Results from a capacity planning case study highlight the quality of the stochastic solutions obtained using MILP DMP models for scenario generation. Furthermore, the proposed game theoretic extension of DMP enhances in-sample and out-of-sample stability with respect to the challenging problem of user-defined parameters variability.

Download activity - last month

Export as JSONCSVXML

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as JSONCSVXML

Archive Staff Only

View Item