Machine Learning-Guided Dual Heuristics ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants
Auteur(s) :
Dupin, Nicolas [Auteur correspondant]
Laboratoire de Recherche en Informatique [LRI]
Talbi, El-Ghazali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Laboratoire de Recherche en Informatique [LRI]
Talbi, El-Ghazali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Titre de la revue :
Algorithms
Pagination :
185
Éditeur :
MDPI
Date de publication :
2020-08
ISSN :
1999-4893
Mot(s)-clé(s) en anglais :
Operations research
Mixed integer programming
stochastic optimization
dual bounds
dual heuristics
hybrid heuristics
Matheuristics
machine learning
EURO/ROADEF Challenge 2010
Maintenance planning
nuclear power plants: power generation
Mixed integer programming
stochastic optimization
dual bounds
dual heuristics
hybrid heuristics
Matheuristics
machine learning
EURO/ROADEF Challenge 2010
Maintenance planning
nuclear power plants: power generation
Discipline(s) HAL :
Informatique [cs]/Recherche opérationnelle [cs.RO]
Résumé en anglais : [en]
This paper studies the hybridization of Mixed Integer Programming (MIP) with dual heuristics and machine learning techniques, to provide dual bounds for a large scale optimization problem from an industrial application. ...
Lire la suite >This paper studies the hybridization of Mixed Integer Programming (MIP) with dual heuristics and machine learning techniques, to provide dual bounds for a large scale optimization problem from an industrial application. The case study is the EURO/ROADEF Challenge 2010, to optimize the refueling and maintenance planning of nuclear power plants. Several MIP relaxations are presented to provide dual bounds computing smaller MIPs than the original problem. It is proven how to get dual bounds with scenario decomposition in the different 2-stage programming MILP formulations, with a selection of scenario guided by machine learning techniques. Several sets of dual bounds are computable, improving significantly the former best dual bounds of the literature and justifying the quality of the best primal solution known.Lire moins >
Lire la suite >This paper studies the hybridization of Mixed Integer Programming (MIP) with dual heuristics and machine learning techniques, to provide dual bounds for a large scale optimization problem from an industrial application. The case study is the EURO/ROADEF Challenge 2010, to optimize the refueling and maintenance planning of nuclear power plants. Several MIP relaxations are presented to provide dual bounds computing smaller MIPs than the original problem. It is proven how to get dual bounds with scenario decomposition in the different 2-stage programming MILP formulations, with a selection of scenario guided by machine learning techniques. Several sets of dual bounds are computable, improving significantly the former best dual bounds of the literature and justifying the quality of the best primal solution known.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
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