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Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants

Nicolas Dupin 1, * El-Ghazali Talbi 2
* Corresponding author
2 BONUS - Optimisation de grande taille et calcul large échelle
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : 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.
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https://hal.inria.fr/hal-03093679
Contributor : Talbi El-Ghazali Connect in order to contact the contributor
Submitted on : Monday, January 4, 2021 - 9:12:27 AM
Last modification on : Friday, January 21, 2022 - 3:10:37 AM

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Nicolas Dupin, El-Ghazali Talbi. Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants. Algorithms, MDPI, 2020, 13 (8), pp.185. ⟨10.3390/a13080185⟩. ⟨hal-03093679⟩

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