S. Pineda, M. Á. Muñoz, J. M. Morales González
We propose a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Given a finite collection of observations of the uncertain parameters and potential explanatory variables, our approach fits a parametric model to those data that is specifically tailored to maximizing the decision value. From a mathematical point of view, our framework translates into a bilevel program that can be solved using off-the-shelf optimization solvers. We showcase the benefits of moving from the traditional scheme for model estimation (based on statistical quality metrics) to decision-guided prediction using three different practical problems. We also compare our approach with existing ones in a realistic case study that considers a strategic power producer that participates in the Iberian electricity market.
Palabras clave: Data-driven decision-making under uncertainty, Bilevel programming, Statistical regression, Strategic producer, Electricity market
Programado
GT04 Análisis Multivariante y Clasificación V. Mathematical Optimization and Data Science
8 de junio de 2022 12:40
Sala de Claustros
