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Business Process Optimization with Reinforcement Learning
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2019 (English)In: Lect. Notes Bus. Inf. Process., Springer Verlag , 2019, Vol. 356, p. 203-212Conference paper, Published paper (Refereed)
Abstract [en]

We investigate the use of deep reinforcement learning to optimize business processes in a business support system. The focus of this paper is to investigate how a reinforcement learning algorithm named Q-Learning, using deep learning, can be configured in order to support optimization of business processes in an environment which includes some degree of uncertainty. We make the investigation possible by implementing a software agent with the help of a deep learning tool set. The study shows that reinforcement learning is a useful technique for business process optimization but more guidance regarding parameter setting is needed in this area. © 2019, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Verlag , 2019. Vol. 356, p. 203-212
Series
Lecture Notes in Business Information Processing ; 356
Keywords [en]
Business process optimization, Deep learning, Reinforcement learning, Learning algorithms, Machine learning, Optimization, Process control, Software agents, Software design, Systems engineering, Business Process, Business support systems, Degree of uncertainty, Parameter setting, Q-learning, Support optimizations
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-18615DOI: 10.1007/978-3-030-24854-3_13Scopus ID: 2-s2.0-85069189424ISBN: 9783030248536 (print)OAI: oai:DiVA.org:bth-18615DiVA, id: diva2:1349856
Conference
9th International Symposium on Business Modeling and Software Design, BMSD 2019; Lisbon; Portugal; 1 July 2019 through 3 July 2019
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-10Bibliographically approved

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Silvander, Johan

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf