A neural network-based distributional constraint learning methodology for mixed-integer stochastic optimization-Reference-Cited by-同舟云学术

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A neural network-based distributional constraint learning methodology for mixed-integer stochastic optimization

Published:2023-12 Issue: Volume:232 Page:120895
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Alcántara Antonio^ORCID,Ruiz Carlos^ORCID

Funder

Ministerio de Ciencia e Innovación

Universidad Carlos III de Madrid

Agencia Estatal de Investigación

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference30 articles.

1. Distcl;Alcántara,2022

2. On data-driven chance constraint learning for mixed-integer optimization problems;Alcántara;Applied Mathematical Modelling,2023

3. Strong mixed-integer programming formulations for trained neural networks;Anderson;Mathematical Programming,2020

4. A data-driven model of virtual power plants in day-ahead unit commitment;Babaei;IEEE Transactions on Power Systems,2019

5. JANOS: an integrated predictive and prescriptive modeling framework;Bergman;INFORMS Journal on Computing,2022

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1. Optimal day-ahead offering strategy for large producers based on market price response learning;European Journal of Operational Research;2024-12

2. Powered stochastic optimization with hypergradient descent for large-scale learning systems;Expert Systems with Applications;2024-03

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