Skip to main content
SpringerLink
Log in

Digital Twin for Decision-Support: An Insight into the Integration of Simulation Models into Digital Twin Architectures

  • Conference paper
  • First Online:
Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future (SOHOMA 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1136))

  • 159 Accesses

Abstract

The fourth industrial revolution aims to achieve greater flexibility and adaptability in manufacturing systems through the use of information and communication technologies. The Digital Twin technology has emerged as a promising solution to support human-centred decision-making in this context. Despite the growing interest in this area, there is still a lack of applications that integrate decision-support functionality and emphasize the relationship between real-time Digital Twin models and what-if simulation models. Hence, this paper discusses the integration of simulation models into a Digital Twin architecture to assist operators in making appropriate decisions. A proof of concept is presented to demonstrate the feasibility of this approach and to open up perspectives for further research 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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. White paper, vol. 1, pp. 1–7 (2014)

    Google Scholar 

  2. Semeraro, C., Lezoche, M., Panetto, H., Dassisti, M.: Digital twin paradigm: a systematic literature review. Comput. Ind. 130, 103469 (2021). https://doi.org/10.1016/j.compind.2021.103469

    Article  Google Scholar 

  3. Villalonga, A., Negri, E., Biscardo, G., et al.: A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins. Annu. Rev. Control. 51, 357–373 (2021). https://doi.org/10.1016/j.arcontrol.2021.04.008

    Article  Google Scholar 

  4. Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and U.S. Air Force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, p. 1818. American Institute of Aeronautics and Astronautics (2012)

    Google Scholar 

  5. Cimino, C., Negri, E., Fumagalli, L.: Review of digital twin applications in manufacturing. Comput. Ind. 113, 103130 (2019). https://doi.org/10.1016/j.compind.2019.103130

    Article  Google Scholar 

  6. Agrawal, A., Thiel, R., Jain, P., et al.: Digital twin: where do humans fit in? Autom. Constr. 148, 104749 (2023). https://doi.org/10.1016/j.autcon.2023.104749

    Article  Google Scholar 

  7. Boschert, S., Rosen, R.: Digital twin–the simulation aspect. In: Hehenberger, P., Bradley, D. (eds.) Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and Their Designers, pp. 59–74. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32156-1_5

    Chapter  Google Scholar 

  8. Schluse, M., Rossmann, J.: From simulation to experimentable digital twins: simulation-based development and operation of complex technical systems. In: 2016 IEEE International Symposium on Systems Engineering (ISSE), pp. 1–6 (2016)

    Google Scholar 

  9. Kaiser, B., Reichle, A., Verl, A.: Model-based automatic generation of digital twin models for the simulation of reconfigurable manufacturing systems for timber construction. Procedia CIRP 107, 387–392 (2022). https://doi.org/10.1016/j.procir.2022.04.063

    Article  Google Scholar 

  10. Tao, F., Zhang, M.: Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5, 20418–20427 (2017). https://doi.org/10.1109/ACCESS.2017.2756069

    Article  Google Scholar 

  11. Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, F.-J., Flumerfelt, S., Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems, pp. 85–113. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-38756-7_4

    Chapter  Google Scholar 

  12. Tao, F., Zhang, M., Liu, Y., Nee, A.Y.C.: Digital twin driven prognostics and health management for complex equipment. CIRP Ann. 67, 169–172 (2018). https://doi.org/10.1016/j.cirp.2018.04.055

    Article  Google Scholar 

  13. Kritzinger, W., Karner, M., Traar, G., et al.: Digital Twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51, 1016–1022 (2018). https://doi.org/10.1016/j.ifacol.2018.08.474

    Article  Google Scholar 

  14. Cardin, O.: Classification of cyber-physical production systems applications: proposition of an analysis framework. Comput. Ind. 104, 11–21 (2019). https://doi.org/10.1016/j.compind.2018.10.002

    Article  Google Scholar 

  15. Cardin, O., Trentesaux, D.: General concepts. In: Digitalization and Control of Industrial Cyber-Physical Systems, pp. 1–16. John Wiley & Sons, Ltd. (2022)

    Google Scholar 

  16. Bouleux, G., et al.: Requirements for a digital twin for an emergency department. In: Borangiu, T., Trentesaux, D., Leitão, P. (eds.) SOHOMA 2022. SCI, vol. 1083, pp. 130–141. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-24291-5_11

    Chapter  Google Scholar 

  17. Sahlab, N., Braun, D., Köhler, C., et al.: Extending the Intelligent Digital Twin with a context modeling service: a decision support use case. Procedia CIRP 107, 463–468 (2022). https://doi.org/10.1016/j.procir.2022.05.009

    Article  Google Scholar 

  18. Coelho, F., Relvas, S., Barbosa-Póvoa, A.P.: Simulation-based decision support tool for in-house logistics: the basis for a digital twin. Comput. Ind. Eng. 153, 107094 (2021). https://doi.org/10.1016/j.cie.2020.107094

    Article  Google Scholar 

  19. Kunath, M., Winkler, H.: Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process. Procedia CIRP 72, 225–231 (2018). https://doi.org/10.1016/j.procir.2018.03.192

    Article  Google Scholar 

  20. Neto, A.A., Carrijo, B.S., Romanzini Brock, J.G., et al.: Digital twin-driven decision support system for opportunistic preventive maintenance scheduling in manufacturing. Procedia Manuf. 55, 439–446 (2021). https://doi.org/10.1016/j.promfg.2021.10.060

    Article  Google Scholar 

  21. dos Santos, C.H., Lima, R.D.C., Leal, F., et al.: A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods. Production 30, e20200018 (2020). https://doi.org/10.1590/0103-6513.20200018

    Article  Google Scholar 

  22. Meierhofer, J., Schweiger, L., Lu, J., et al.: Digital twin-enabled decision support services in industrial ecosystems. Appl. Sci. 11, 11418 (2021). https://doi.org/10.3390/app112311418

    Article  Google Scholar 

  23. Korth, B., Schwede, C., Zajac, M.: Simulation-ready digital twin for realtime management of logistics systems. In: 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, pp. 4194–4201. IEEE (2018)

    Google Scholar 

  24. Moyaux, T., Liu, Y., Bouleux, G., Cheutet, V.: An Agent-based architecture of the Digital Twin for an Emergency Department. Sustainability 15, 3412 (2023). https://doi.org/10.3390/su15043412

    Article  Google Scholar 

  25. Pires, F., Souza, M., Ahmad, B., Leitão, P.: Decision support based on digital twin simulation: a case study. In: Borangiu, T., Trentesaux, D., Leitão, P., et al. (eds.) SOHOMA 2020. SCI, vol. 952, pp. 99–110. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69373-2_6

    Chapter  Google Scholar 

  26. dos Santos, C.H., Montevechi, J.A.B., de Queiroz, J.A., et al.: Decision support in productive processes through DES and ABS in the Digital Twin era: a systematic literature review. Int. J. Prod. Res. 60, 2662–2681 (2022). https://doi.org/10.1080/00207543.2021.1898691

    Article  Google Scholar 

  27. Murphy, A., Taylor, C., Acheson, C., et al.: Representing financial data streams in digital simulations to support data flow design for a future Digital Twin. Robot. Comput.-Integr. Manuf. 61, 101853 (2020). https://doi.org/10.1016/j.rcim.2019.101853

    Article  Google Scholar 

  28. Karakra, A., Fontanili, F., Lamine, E., et al.: Pervasive computing integrated discrete event simulation for a hospital digital twin. In: 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1–6 (2018)

    Google Scholar 

  29. Katz, D., Manivannan, S.: Exception management on a shop floor using online simulation. In: Proceedings of 1993 Winter Simulation Conference (WSC 1993), pp. 888–896 (1993)

    Google Scholar 

  30. Cardin, O.: Contribution of online simulation to production activity control decision support-application to a flexible manufacture system. Ph.D. thesis, Université de Nantes, Nantes (2007)

    Google Scholar 

  31. Tao, F., Sui, F., Liu, A., et al.: Digital twin-driven product design framework. Int. J. Prod. Res. 57, 3935–3953 (2019)

    Article  Google Scholar 

  32. Abdoune, F., Cardin, O., Nouiri, M., Castagna, P.: Real-time field synchronization mechanism for Digital Twin manufacturing systems. IFAC-PapersOnLine 56(2), 5649–5654 (2023)

    Article  Google Scholar 

Download references

Acknowledgment

The authors would like to thank the GDR MACS for funding the short-term mobility that allowed this study to take place.

Author information

Authors and Affiliations

  1. Nantes University, École Centrale Nantes, CNRS, LS2N, UMR 6004, 44000, Nantes, France

    Farah Abdoune, Maroua Nouiri & Olivier Cardin

  2. University Lyon, INSA Lyon, Univ. Jean Monnet Saint-Étienne, Université Claude Bernard Lyon 1, Université Lumière Lyon 2, DISP UR4570 , Villeurbanne, France

    Vincent Cheutet

Authors
  1. Farah Abdoune
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincent Cheutet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maroua Nouiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olivier Cardin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vincent Cheutet .

Editor information

Editors and Affiliations

  1. Faculty of Automatic Control and Computer Science (Automatica), University Politehnica of Bucharest, Bucharest, Romania

    Theodor Borangiu

  2. UPHF, LAMIH UMR CNRS 8201, Université Polytechnique Hauts de France, Valenciennes cedex 9, France

    Damien Trentesaux

  3. Polytechnic Institute of Bragança, Braganca, Portugal

    Paulo Leitão

  4. Polytech Annecy-Chambéry, LISTIC, Annecy, France

    Lamia Berrah

  5. SYMME, Polytech Annecy-Chambéry, Annecy, France

    Jose-Fernando Jimenez

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

Abdoune, F., Cheutet, V., Nouiri, M., Cardin, O. (2024). Digital Twin for Decision-Support: An Insight into the Integration of Simulation Models into Digital Twin Architectures. In: Borangiu, T., Trentesaux, D., Leitão, P., Berrah, L., Jimenez, JF. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2023. Studies in Computational Intelligence, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-031-53445-4_2

Download citation

Publish with us

Policies and ethics

Search

Navigation