A reinforcement learning approach for a lot sizing and production scheduling problem with energy consideration - l'unam - université nantes angers le mans Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

A reinforcement learning approach for a lot sizing and production scheduling problem with energy consideration

Résumé

With climate change, many companies are looking to reduce their carbon footprint and ensure a sustainable manufacturing. To meet this challenge, one of the alternatives is to replace carbon intensive processes with low-carbon processes involving electrical and/or renewable energies. Within this scope, a novel scheduling approach is proposed to take into account the introduction of onsite renewable energy. In particular, a lot sizing and production-scheduling problem in flexible flow line with renewable energy integration is formulated as a versatile optimization model. With regard to associated complexity issues, a multi-agent reinforcement learning approach is advocated to solve the lot sizing and scheduling problem. Finally, the approach is evaluated with a benchmark case and other numerical experiments.
Fichier principal
Vignette du fichier
IFAC_Papers.pdf (997.94 Ko) Télécharger le fichier
Origine Publication financée par une institution
licence

Dates et versions

hal-04331801 , version 1 (24-05-2024)

Licence

Identifiants

Citer

Mohamed Habib Jabeur, Sonia Mahjoub, Cyril Toublanc, Veronique Cariou. A reinforcement learning approach for a lot sizing and production scheduling problem with energy consideration. 22nd IFAC World Congress, Jul 2023, Yokohama, Japan. pp.11141-11147, ⟨10.1016/j.ifacol.2023.10.832⟩. ⟨hal-04331801⟩
22 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More