Skip to main content
SpringerLink
Log in

Symbiotic Simulation System (S3) for Industry 4.0

  • Chapter
  • First Online:
Simulation for Industry 4.0

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

Abstract

This chapter discusses symbiotic simulation system , a simulation system that is designed to support online short-term operations management decision. The prevalence of real-time data and the advances in Industry 4.0 technologies have made the real-world implementation of the vision of using simulation to support real-time decision making a reality. The main contributions of this chapter are to provide a review of similar concepts in simulation, to provide the architecture of symbiotic simulation system at the conceptual level, to classify the types of symbiotic simulation applications, and to highlights research challenges in symbiotic simulation .

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
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. Rogers P, Gordon RJ (1993) Simulation for real-time decision making in manufacturing systems. In: Proceedings of the winter simulation conference, pp 866–874

    Google Scholar 

  2. Davis W (1998) On-line simulation: Need and evolving research requirements. In: Banks J (ed) Handbook of simulation. Wiley, New York, pp 465–516

    Chapter  Google Scholar 

  3. Rowson JA (1994) Hardware/software co-simulation. In: Proceedings of the design automation conference, pp 439–440

    Google Scholar 

  4. Bohlmann S, Szczerbicka H, Klinger V (2010) Co-simulation in large scale environments using the HPNS framework. In: Proceedings of the 2010 conference on grand challenges in modeling & simulation, pp 211–218

    Google Scholar 

  5. Darema F (2004) Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak M, van Albada GD, Sloot PMA, Dongarra J (eds) Computational science—ICCS 2004. ICCS 2004. Lecture notes in computer science, vol 3038. Springer, Berlin, Heidelberg

    Chapter  Google Scholar 

  6. Fujimoto R, Lunceford D, Page E, Uhrmacher AM (2002) Grand challenges for modeling and simulation: Dagstuhl report. Technical report 350, Schloss Dagstuhl. Seminar No 02351

    Google Scholar 

  7. Aydt H, Turner SJ, Cai W, Low MYH (2008) Symbiotic simulation systems: an extended definition motivated by symbiosis in biology. In: Proceedings of the 22nd workshop on principles of advanced and distributed simulation, pp 109–116

    Google Scholar 

  8. Szozda N (2017) Industry 4.0 and its impact on the functioning of supply chains. LogForum 13(4):401–414

    Google Scholar 

  9. Xu LD, Xu EL, Li L (2018) Industry 4.0: state of the art and future trends. Int J Prod Res 56(8):2941–2962

    Google Scholar 

  10. Yin Y, Stecke KE, Li D-n (2018) The evolution of production systems from Industry 2.0 through Industry 4.0. Int J Prod Res 56(1–2):848–861

    Article  Google Scholar 

  11. Monostori L (2014) Cyber-physical production systems: roots, expectations and R&D challenges. Proc CIRP 17:9–13

    Article  Google Scholar 

  12. Lanner (2017) Industry 4.0: using simulation and the predictive digital twin to support successful digital transformation. Retrieved from https://marketing.lanner.com/acton/attachment/5613/f-01c2/1/-/-/-/-/Industry%204.pdf. Accessed on 03 Oct 2018

  13. Flexsim (2018) FlexSim involved in advancements with Industry 4.0 technologies. Retrieved from https://www.flexsim.com/flexsim-involved-in-advancements-with-industry-4-0-technologies/. Accessed on 03 Oct 2018

  14. AnyLogic (2017) CNH industrial digital twin of a manufacturing line: helping maintenance decision-making. Retrieved from https://www.anylogic.com/digital-twin-of-a-manufacturing-line-helping-maintenance-decision-making/. Accessed on 03 Oct 2018

  15. Grieves DM (2014) Digital twin: manufacturing excellence through virtual factory replication. Florida Institute of Technology, Center for Lifecycle and Innovation Management. Retrieved from http://innovate.fit.edu/plm/documents/doc_mgr/912/1411.0_Digital_Twin_White_Paper_Dr_Grieves.pdf. Accessed on 03 Oct 2018

  16. Onggo BS, Mustafee N, Juan AA, Molloy O, Smart A (2018) Symbiotic simulation system: hybrid systems model meets big data analytics. In: Proceedings of the 2018 winter simulation conference, pp 1358–1369

    Google Scholar 

  17. Fu MC (2015) Handbook of simulation optimization, 1st edn. Springer, New York, NY

    MATH  Google Scholar 

  18. Juan AA, Faulin J, Grasman SE, Rabe M, Figueira G (2015) A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems. Oper Res Perspect 2:62–72

    Article  MathSciNet  Google Scholar 

  19. Rhodes-Leader L, Onggo BS, Worthington DJ, Nelson BL (2018b) Multi-fidelity simulation optimisation for airline disruption management. In: Proceedings of the 2018 simulation workshop, pp 2179–2190

    Google Scholar 

  20. Panadero J, Juan AA, Mozos JM, Corlu CG, Onggo BS (2018) Agent-based simheuristics: extending simulation-optimization algorithms via distributed and parallel computing. In: Proceedings of the 2018 winter simulation conference, pp 869–880

    Google Scholar 

  21. Onggo BS, Karatas M (2016) Test-Driven simulation modelling: a case study using agent-based maritime search-operation simulation. Eur J Oper Res 254(2):517–531

    Article  Google Scholar 

  22. Moeuf A, Pellerin R, Lamouri S, Tamayo-Giraldo S, Barbaray R (2017) The industrial management of SMEs in the era of industry 4.0. Int J Prod Res 56(3):1118–1136

    Article  Google Scholar 

  23. Katz D, Manivannan S (1993) Exception management on a shop floor using online simulation. In: Proceedings of the winter simulation conference, pp 888–896

    Google Scholar 

  24. Oakley D, Onggo BSS, and Worthington DJ (2019) Symbiotic Simulation for the Operational Management of Inpatient Beds: Model Development and Validation using Δ-Method. Health Care Management Science. In press.

    Google Scholar 

  25. Patrikalakis NM, McCarthy JJ, Robinson AR, Schmidt H, Evange-linos C, Haley PJ, Lalis S, Lermusiaux PFJ, Tian R, Leslie WG, Cho W (2004) Towards a dynamic data driven system for rapid adaptive interdisciplinary ocean forecasting. Massachusetts Institute of Technology, Cambridge, MA, USA. Retrieved from http://czms.mit.edu/poseidon/new1/publications/kluwer.pdf. Accessed on 03 Oct 2018

  26. Rhodes-Leader L, Onggo BS, Worthington DJ, Nelson BL (2018a). Airline disruption recovery using symbiotic simulation and multi-fidelity modelling. In: Proceedings of the 2018 simulation workshop, pp 146–155

    Google Scholar 

  27. Parashar M, Klie H, Ctalynrek U, Kurc T, Bangerth W, Matossian V, Saltz J, Wheeler MF (2004) Application of grid–enabled technologies for solving optimization problems in data-driven reservoir studies. Future Gener Comput Syst 21:19–26

    Article  Google Scholar 

  28. Kotiadis K (2016) Towards self-adaptive discrete event simulation (SADES). In: Proceedings of the operational research society simulation workshop, pp 181–191

    Google Scholar 

  29. McKinsey (2015) Industry 4.0: How to navigate digitization of the manufacturing sector. Retrieved from https://www.mckinsey.com/business-functions/operations/our-insights/industry-four-point-o-how-to-navigae-the-digitization-of-the-manufacturing-sector. Accessed on 24 Sept 2018

Download references

Author information

Authors and Affiliations

  1. Southampton Business School, Centre for Operational Research, Management Sciences and Information Systems, University of Southampton, Southampton, UK

    Bhakti Stephan Onggo

Authors
  1. Bhakti Stephan Onggo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bhakti Stephan Onggo .

Editor information

Editors and Affiliations

  1. Barbaros Naval Science and Engineering Institute, National Defense University Tuzla, Istanbul, Turkey

    Murat M. Gunal

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Onggo, B.S. (2019). Symbiotic Simulation System (S3) for Industry 4.0. In: Gunal, M. (eds) Simulation for Industry 4.0. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-04137-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04137-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04136-6

  • Online ISBN: 978-3-030-04137-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation