Alternating direction method and deep learning for discrete control with storage - Mines Paris
Chapitre D'ouvrage Année : 2024

Alternating direction method and deep learning for discrete control with storage

Résumé

This paper deals with scheduling the operations in systems with storage modeled as a mixed integer nonlinear program (MINLP). Due to time interdependency induced by storage, discrete control, and nonlinear operational conditions, computing even a feasible solution may require an unaffordable computational burden. We exploit a property common to a broad class of these problems to devise a decomposition algorithm related to alternating direction methods, which progressively adjusts the operations to the storage state profile. We also design a deep learning model to predict the continuous storage states to start the algorithm instead of the discrete decisions, as commonly done in the literature. This enables search diversification through a multi-start mechanism and prediction using scaling in the absence of a training set. Numerical experiments on the pump scheduling problem in water networks show the effectiveness of this hybrid learning/decomposition algorithm in computing near-optimal strict-feasible solutions in more reasonable times than other approaches.
Fichier principal
Vignette du fichier
isco24-demassey.pdf (570.82 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04506597 , version 1 (15-03-2024)

Licence

Identifiants

Citer

Sophie Demassey, Valentina Sessa, Amirhossein Tavakoli. Alternating direction method and deep learning for discrete control with storage. Combinatorial Optimization, 14594, Springer Nature Switzerland, pp.85-96, 2024, Lecture Notes in Computer Science, ⟨10.1007/978-3-031-60924-4_7⟩. ⟨hal-04506597⟩
245 Consultations
115 Téléchargements

Altmetric

Partager

More