A Quantile Neural Network Framework for Two-stage Stochastic Optimization
A. Alcántara Mata, C. Ruiz Mora, C. Tsay
Two-stage stochastic problems are often formulated using Sample Average Approximation (SAA), where uncertainty is modeled as a finite set of scenarios, resulting in a large monolithic problem. This models can be challenging to solve, and several problem-specific decomposition approaches have been proposed. An alternative approach is to approximate the expected second-stage objective value using a surrogate model, which can then be embedded in the first-stage problem to produce good heuristic solutions. In this work, we propose to instead model the distribution of the second-stage objective, specifically using a quantile neural network (QNN). Embedding this distributional approximation enables capturing uncertainty and is not limited to expected-value optimization. We discuss optimization formulations for embedding the QNN and demonstrate the effectiveness of the proposed framework using several computational case studies including a set of MIL optimization problems.
Keywords: Optimization under Uncertainty, Stochastic Programming, Neural Networks, Mixed-Integer Programming
Scheduled
Methods and Applications of OR III
June 13, 2025 11:00 AM
Sala de prensa (MR 13)
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