Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Resource Optimization in Business Processes

  • Conference paper
  • First Online:
Enterprise, Business-Process and Information Systems Modeling (BPMDS 2024, EMMSAD 2024)

Abstract

In administrative processes, such as financial or governmental processes, humans typically do most of the work and must be allocated to tasks in an efficient manner. This allocation is made complicated by the different authorizations and the varying effectiveness of people for tasks. Moreover, administrative processes operate under substantial uncertainty, as the customer’s journey through the process typically is uncertain upon their arrival. To help solve this problem, we present a framework for resource optimization in administrative processes and delineate its differences from existing resource allocation models. We proceed to show several resource allocation solutions that have been developed with the framework. We specifically address the challenges that are encountered when implementing these solutions, some of which remain unresolved. By doing so we aim to shed light on promising avenues for future research in this domain.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/bpogroup/aepn-project.

  2. 2.

    https://github.com/bpogroup/simpn.

References

  1. Cals, B., Zhang, Y., Dijkman, R., Dorst, C.: Solving the online batching problem using deep reinforcement learning. Comput. Ind. Eng. 156 (2021)

    Google Scholar 

  2. Farahani, A., van Elzakker, M.A.H., Genga, L., Troubil, P., Dijkman, R.M.: Relational graph attention-based deep reinforcement learning: an application to flexible job shop scheduling with sequence-dependent setup times. In: Proceedings of International Conference on Learning and Intelligent Optimization (LION), pp. 347–362 (2023)

    Google Scholar 

  3. Farahani, A., Genga, L., Dijkman, R.M.: Tackling uncertainty in online multimodal transportation planning using deep reinforcement learning. In: Proceedings of International Conference on Computational Logistics (ICCL) (2021)

    Google Scholar 

  4. Gumuskaya, V., van Jaarsveld, W., Dijkman, R., Grefen, P., Veenstra, A.: Integrating stochastic programs and decision trees in capacitated barge planning with uncertain container arrivals. Transp. Res. Part C 132 (2021)

    Google Scholar 

  5. Kubrak, K., Milani, F., Nolte, A., Dumas, M.: Prescriptive process monitoring: Quo Vadis? PeerJ Comput. Sci. 8, e1097 (2022)

    Article  Google Scholar 

  6. Lo Bianco, R., Dijkman, R.M., Nuijten, W., van Jaarsveld, W.: Action-evolution petri nets: a framework for modeling and solving dynamic task assignment problems. In: International Conference on Business Process Management (BPM), pp. 216–231 (2023)

    Google Scholar 

  7. Middelhuis, J., Lo Bianco, R., Scherzer, E., Bukhsh, Z.A., Adan, I.J., Dijkman, R.M.: Learning policies for resource allocation in business processes. arXiv preprint arXiv:2304.09970 (2023)

  8. Temizöz, T., Imdahl, C., Dijkman, R., Lamghari-Idrissi, D., van Jaarsveld, W.: Deep controlled learning for inventory control. arXiv preprint 2011.15122 (2023)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Remco Dijkman .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dijkman, R. (2024). Resource Optimization in Business Processes. In: van der Aa, H., Bork, D., Schmidt, R., Sturm, A. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2024 2024. Lecture Notes in Business Information Processing, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-031-61007-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-61007-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-61006-6

  • Online ISBN: 978-3-031-61007-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics