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Designing Optimal Robotic Process Automation Architectures

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Service-Oriented Computing (ICSOC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12571))

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Abstract

The design and implementation of Robotic process automation (RPA) requires an architecture where there is seamless coordination between humans, robotic agents, and intelligent agents automating information acquisition tasks and decision-making tasks. Effective coordination of agents would need to consider the efficiency of different types of resources in completing tasks, the quality when handling complex tasks, and the cost of resources executing the task. In this work, a novel approach for generating an optimal architecture considering distinct types of resources that include human, intelligent and robotic agents is proposed. An optimal architecture is the optimal enactment of process instances executed by a combination of human and automation agents based on their characteristics. The architecture considers resources, resource types, and their characteristics that meet multiple objectives of process execution.

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References

  1. van der Aalst, W.M.P., Bichler, M., Heinzl, A.: Robotic process automation. BISE, pp. 269–272 (2018)

    Google Scholar 

  2. Arias, M., Munoz-Gama, J., Sepúlveda, M.: Towards a taxonomy of human resource allocation criteria. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 475–483. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_37

    Chapter  Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  4. Diao, Y., Heching, A.: Staffing optimization in complex service delivery systems. In: 7th International Conference CNSM 2011, Paris, France, October 24–28, 2011, pp. 1–9. IEEE (2011). http://ieeexplore.ieee.org/document/6104024/

  5. Diao, Y., Heching, A.R., Northcutt, D.M., Wallace, R.: Service-delivery modeling and optimization. Interfaces 45(3), 243–259 (2015)

    Article  Google Scholar 

  6. Havur, G., Cabanillas, C., Mendling, J., Polleres, A.: Resource allocation with dependencies in business process management systems. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNBIP, vol. 260, pp. 3–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45468-9_1

    Chapter  Google Scholar 

  7. Huang, Z., Lu, X., Duan, H.: Resource behavior measure and application in business process management. Expert Syst. Appl. 39(7), 6458–6468 (2012)

    Article  Google Scholar 

  8. Jiménez-Ramírez, A., Weber, B., Barba, I., Valle, C.D.: Generating optimized configurable business process models in scenarios subject to uncertainty. Inf. Softw. Technol. 57, 571–594 (2015)

    Article  Google Scholar 

  9. Kabicher-Fuchs, S., Mangler, J., Rinderle-Ma, S.: Experience breeding in process-aware information systems. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 594–609. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38709-8_38

    Chapter  Google Scholar 

  10. Kumar, A., Dijkman, R., Song, M.: Optimal resource assignment in workflows for maximizing cooperation. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 235–250. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40176-3_20

    Chapter  Google Scholar 

  11. Kumar, A., et al.: Dynamic work distribution in workflow management systems: How to balance quality and performance. J. Manage. Inf. Syst. 18(3), 157–194 (2002)

    Article  Google Scholar 

  12. Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. Part A 30(3), 286–297 (2000)

    Article  Google Scholar 

  13. Pika, A., Leyer, M., Wynn, M.T., Fidge, C.J., ter Hofstede, A.H.M., van der Aalst, W.M.P.: Mining resource profiles from event logs. ACM Trans. Management Inf. Syst. 8(1), 1:1–1:30 (2017)

    Article  Google Scholar 

  14. Jiménez-Ramírez, A., Barba, I., del Valle, C., Weber, B.: Generating multi-objective optimized business process enactment plans. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 99–115. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38709-8_7

    Chapter  Google Scholar 

  15. Jimenez-Ramirez, A., Reijers, H.A., Barba, I., Del Valle, C.: A method to improve the early stages of the robotic process automation lifecycle. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 446–461. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_28

    Chapter  Google Scholar 

  16. Reichert, M., Weber, B.: Enabling Flexibility in Process-Aware Information Systems - Challenges, Methods, Technologies (2012)

    Google Scholar 

  17. Russell, S.J., Norvig, P.: Artificial Intelligence - A Modern Approach (3. internat. ed.). Pearson Education (2010)

    Google Scholar 

  18. Scheepers, R., Lacity, M.C., Willcocks, L.P.: Cognitive automation as part of deakin university’s digital strategy. MIS Q. Executive 17(2), 89–107 (2018)

    Google Scholar 

  19. Sindhgatta, R., ter Hofstede, A.H.M., Ghose, A.: Resource-based adaptive robotic process automation. In: Dustdar, S., Yu, E., Salinesi, C., Rieu, D., Pant, V. (eds.) CAiSE 2020. LNCS, vol. 12127, pp. 451–466. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49435-3_28

    Chapter  Google Scholar 

  20. Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015–1021. Springer, Heidelberg (2006). https://doi.org/10.1007/11941439_114

    Chapter  Google Scholar 

  21. Vagia, M., Transeth, A.A., Fjerdingen, S.A.: A literature review on the levels of automation during the years. what are the different taxonomies that have been proposed? Appl. Ergon. 53, 190–202 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Wollongong, Wollongong, Australia

    Geeta Mahala, Hoa Khanh Dam & Aditya Ghose

  2. Queensland University of Technology, Brisbane, Australia

    Renuka Sindhgatta

Authors
  1. Geeta Mahala
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  2. Renuka Sindhgatta
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  3. Hoa Khanh Dam
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  4. Aditya Ghose
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Corresponding author

Correspondence to Geeta Mahala .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Eleanna Kafeza

  2. University of New South Wales, Sydney, NSW, Australia

    Boualem Benatallah

  3. IIT National Research Council C.N.R., Pisa, Italy

    Fabio Martinelli

  4. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  5. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  6. Ernst & Young AI Lab, San Jose, CA, USA

    Hamid Motahari

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Mahala, G., Sindhgatta, R., Dam, H.K., Ghose, A. (2020). Designing Optimal Robotic Process Automation Architectures. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_32

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  • DOI: https://doi.org/10.1007/978-3-030-65310-1_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65309-5

  • Online ISBN: 978-3-030-65310-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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