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Discovering Heuristics And Metaheuristics For Job Shop Scheduling From Scratch Via Deep Reinforcement Learning

Scheduling is the mathematical problem of allocating tasks to resources considering certain constraints. The goal is to achieve the best possible scheduling quality given a quality metric like makespan. Typical scheduling problems, including the classic Job Shop Scheduling Problem (JSP or JSSP), are...

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Autori principali: van Ekeris, Tilo, Meyes, Richard, Meisen, Tobias, Herberger, David, Hübner, Marco
Natura: Artigo
Lingua:Inglês
Pubblicazione: TIB – Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek 2021-01-01
Serie:Proceedings of the Conference on Production Systems and Logistics
Soggetti:
deep reinforcement learning (drl)
production planning
scheduling
job shop scheduling (jsp, jssp)
proximal policy optimization (ppo)
heuristics
metaheuristics
konferenzschrift
Accesso online:https://repo.uni-hannover.de/handle/123456789/11318
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