A DRL based approach for adaptive scheduling of one-of-a-kind production

DOI10.1016/j.cor.2023.106306OpenAlexW4380793067MaRDI QIDQ6106605

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Publication date: 3 July 2023

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2023.106306

zbMATH Keywords

priority ruleadaptive schedulingdeep reinforcement learningone-of-a-kind production

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Cites Work

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• A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints
• \({\mathcal Q}\)-learning
• A survey of hybrid metaheuristics for the resource-constrained project scheduling problem
• Operator allocation planning for reconfigurable production line in one-of-a-kind production
• On some implementations of solving the resource constrained project scheduling problems
• New Benchmark Results for the Resource-Constrained Project Scheduling Problem
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