Machine Learning-Guided Dual Heuristics ...
Document type :
Compte-rendu et recension critique d'ouvrage
DOI :
Title :
Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants
Author(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]
Journal title :
Algorithms
Pages :
185
Publisher :
MDPI
Publication date :
2020-08
ISSN :
1999-4893
English keyword(s) :
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
HAL domain(s) :
Informatique [cs]/Recherche opérationnelle [cs.RO]
English abstract : [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. ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
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