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oai:riuma.uma.es:10630/234342026-02-03T11:05:52Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Morales-González, Juan Miguel author Pineda-Morente, Salvador author Muñoz Díaz, Miguel Ángel author 2021-11-22 In this paper, 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 (i.e., the contextual information), our approach fits a parametric model to those data that is specifically tailored to maximizing the decision value, while accounting for possible feasibility constraints. From a mathematical point of view, our framework translates into a bilevel program, for which we provide both a fast regularization procedure and a big-M-based reformulation 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. Finally, we use these numerical simulations to analyze the conditions (in terms of the firm’s cost structure and production capacity) under which our approach proves to be more advantageous to the producer. M.A. Muñoz, S. Pineda, J.M. Morales, A bilevel framework for decision-making under uncertainty with contextual information, Omega, Volume 108, 2022, 102575, ISSN 0305-0483, https://doi.org/10.1016/j.omega.2021.102575. https://hdl.handle.net/10630/23434 https://doi.org/10.1016/j.omega.2021.102575 Toma de decisiones - Modelos matemáticos Estadística A bilevel framework for decision-making under uncertainty with contextual information