Reinforcement Learning for Short-Term Production Scheduling with Sequence-Dependent Setup Waste

Reinforcement Learning for Short-Term Production Scheduling with Sequence-Dependent Setup Waste. Samsonov, V., Behery, M., & Lakemeyer, G. ERCIM News, 122:38–39, July, 2020.

Paper abstract bibtex

Continually refined and adjusted methods for production planning are among the cornerstones of manufacturing excellence. Heuristics and metaheuristic methods developed to address these tasks are often hard to deploy or lead to suboptimal results under constantly changing conditions combined with short response times of modern production planning. Within the DFG-funded Cluster of Excellence “Internet of Production”, a team of researchers from RWTH Aachen University is investigating the use of novel deep learning algorithms to facilitate complex decision-making processes along the manufacturing chain

@article{samsonov2020reinforcement,
  title = {Reinforcement {Learning} for {Short}-{Term} {Production} {Scheduling} with {Sequence}-{Dependent} {Setup} {Waste}},
  volume = {122},
  url = {https://ercim-news.ercim.eu/},
  abstract = {Continually refined and adjusted methods for production planning are among the cornerstones of
manufacturing excellence. Heuristics and metaheuristic methods developed to address these tasks are often
hard to deploy or lead to suboptimal results under constantly changing conditions combined with short
response times of modern production planning. Within the DFG-funded Cluster of Excellence “Internet of
Production”, a team of researchers from RWTH Aachen University is investigating the use of novel deep
learning algorithms to facilitate complex decision-making processes along the manufacturing chain},
  language = {English},
  journal = {ERCIM News},
  author = {Vladimir Samsonov and Mohamed Behery and Gerhard Lakemeyer},
  month = jul,
  year = {2020},
  pages = {38--39}
}

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