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Energy-Flexible Job-Shop Scheduling Using Deep Reinforcement Learning

Considering its high energy demand, the manufacturing industry has grand potential for demand response studies to increase the use of clean energy while reducing its own electricity cost. Production scheduling, driven by smart demand response services, plays a major role in adjusting the manufacturi...

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Huvudupphov: Felder, Mine, Steiner, Daniel, Busch, Paul, Trat, Martin, Sun, Chenwei, Bender, Janek, Ovtcharova, Jivka, Herberger, David, Hübner, Marco, Stich, Volker
Materialtyp: Artigo
Språk:Inglês
Utgiven: TIB – Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek 2023-01-01
Serie:Proceedings of the Conference on Production Systems and Logistics
Ämnen:
deep reinforcement learning
production planning
job-shop scheduling
demand response
energy flexibility
konferenzschrift
Länkar:https://repo.uni-hannover.de/handle/123456789/13564
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