Skip to main content
SpringerLink
Log in

Business Process Optimization with Reinforcement Learning

  • Conference paper
  • First Online:
Book cover Business Modeling and Software Design (BMSD 2019)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aspray, W., Keil-Slawik, R., Parnas, D.L.: Position papers for Dagstuhl seminar 9635 on history of software engineering. Hist. Softw. Eng., 61 (1997)

    Google Scholar 

  2. Born, M., Brelage, C., Markovic, I., Pfeiffer, D., Weber, I.: Auto-completion for executable business process models. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 510–515. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00328-8_51

    Chapter  Google Scholar 

  3. Borrajo, F., Bueno, Y., de Pablo, I., Santos, B., Fernández, F., García, J., Sagredo, I.: SIMBA: a simulator for business education and research. Decis. Support Syst. 48(3), 498–506 (2010)

    Article  Google Scholar 

  4. Geman, S.: Hierarchy in machine and natural vision. In: Proceedings of the 11th Scandinavian Conference on Image Analysis, pp. 1–13 (1999)

    Google Scholar 

  5. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  6. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in the information systems research. MSI Q. 28(1), 75–105 (2004)

    Article  Google Scholar 

  7. Huang, Z., Van Der Aalst, W.M., Lu, X., Duan, H.: Reinforcement learning based resource allocation in business process management. Data Knowl. Eng. 70(1), 127–145 (2011)

    Article  Google Scholar 

  8. Keras: Keras (2018). https://keras.io/. Accessed 30 Nov 2018

  9. Kochenderfer, M.: Decision Making Under Uncertainty. MIT Press, Cambridge (2015)

    Book  Google Scholar 

  10. Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. ArXiv e-prints, September 2015. arXiv:1509.02971

  11. Lin, F.R., Pai, Y.H.: Using multi-agent simulation and learning to design new business processes. IEEE Trans. Syst. Man Cybernet. Part A (Syst. Hum.) 30(3), 380–384 (2000)

    Article  Google Scholar 

  12. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529 (2015)

    Article  Google Scholar 

  13. OpenAI: OpenAI Gym (2018). https://gym.openai.com/. Accessed 30 Nov 2018

  14. Pask, G.: Conversation Theory. Elsevier, Amsterdam (1976)

    MATH  Google Scholar 

  15. Silvander, J., Wilson, M., Wnuk, K.: Encouraging business flexibility by improved context descriptions. In: Shishkov, B. (ed.) Proceedings of the Seventh International Symposium on Business Modeling and Software Design, Barcelona, ScitePress, pp. 225–228 (2017)

    Google Scholar 

  16. Silvander, J., Wilson, M., Wnuk, K., Svahnberg, M.: Supporting continuous changes to business intents. Int. J. Softw. Eng. Knowl. Eng. 27(8), 1167–1198 (2017)

    Article  Google Scholar 

  17. Sutton, R., Barto, A.: Reinforcement Learning: An Introduction, 2nd edn. A Bradford Book, Hardcover (2018)

    MATH  Google Scholar 

  18. Tensorflow: Tensorflow (2018). https://www.tensorflow.org/. Accessed 30 Nov 2018

  19. Wang, H., Zhou, X., Zhou, X., Liu, W., Li, W., Bouguettaya, A.: Adaptive service composition based on reinforcement learning. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 92–107. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17358-5_7

    Chapter  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. Software Engineering Research Lab Sweden, Blekinge Institute of Technology, Karlskrona, Sweden

    Johan Silvander

Authors
  1. Johan Silvander
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Johan Silvander .

Editor information

Editors and Affiliations

  1. Department of Information Systems and Technologies, Faculty of Information Sciences, University of Library Studies and Information Technologies, Sofia, Bulgaria

    Boris Shishkov

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24854-3_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24853-6

  • Online ISBN: 978-3-030-24854-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation