Business Process Optimization with Reinforcement Learning

Johan Silvander⁷

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 356))

Included in the following conference series:

International Symposium on Business Modeling and Software Design

Abstract

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.

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Acknowledgements

We would like to thank the reviewers for their valuable comments.

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Authors and Affiliations

Software Engineering Research Lab Sweden, Blekinge Institute of Technology, Karlskrona, Sweden
Johan Silvander

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Johan Silvander
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Correspondence to Johan Silvander .

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Editors and Affiliations

Department of Information Systems and Technologies, Faculty of Information Sciences, University of Library Studies and Information Technologies, Sofia, Bulgaria
Boris Shishkov

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Silvander, J. (2019). Business Process Optimization with Reinforcement Learning. In: Shishkov, B. (eds) Business Modeling and Software Design. BMSD 2019. Lecture Notes in Business Information Processing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-030-24854-3_13

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DOI: https://doi.org/10.1007/978-3-030-24854-3_13
Published: 04 July 2019
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24853-6
Online ISBN: 978-3-030-24854-3
eBook Packages: Computer ScienceComputer Science (R0)

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