Industrial load management using multi-agent reinforcement learning for rescheduling

Martin Roesch, Christian Linder, Christian Bruckdorfer, Andrea Hohmann, Gunther Reinhart

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Industrial load management plays an important role in the balance of energy consumption and electricity generation, which is increasingly fluctuating due to the growing share of renewable energies. Manufacturing companies are able to adapt their energy consumption by considering energy aspects in their production schedule. It may even be beneficial to temporarily force production resources into idle states and to thus reduce energy demand for a limited period. However, the resulting scheduling problem is very complex and at the same time should be executed in real-time. This paper presents an approach for industrial load management using multi-agent reinforcement learning for energy-oriented rescheduling. A simulation study serves to validate the approach. The results show good solutions and at the same time low computational expense compared to a metaheuristic approach using simulated annealing.

Original languageEnglish
Title of host publicationProceedings - 2019 2nd International Conference on Artificial Intelligence for Industries, AI4I 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages99-102
Number of pages4
ISBN (Electronic)9781728140872
DOIs
StatePublished - Sep 2019
Externally publishedYes
Event2nd International Conference on Artificial Intelligence for Industries, AI4I 2019 - Laguna Hills, United States
Duration: 25 Sep 201927 Sep 2019

Publication series

NameProceedings - 2019 2nd International Conference on Artificial Intelligence for Industries, AI4I 2019

Conference

Conference2nd International Conference on Artificial Intelligence for Industries, AI4I 2019
Country/TerritoryUnited States
CityLaguna Hills
Period25/09/1927/09/19

Keywords

  • Industrial load management
  • Multi-agent reinforcement learning
  • Rescheduling

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