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Architecture for Production Internet

  • Stanisław StrzelczakEmail author
  • Stanisław Marciniak
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 803)

Abstract

Production Internet as a large-scale virtual ecosystem of interacting clients, firms and Things considers the structures of products and services along the inter-organizational coordination, consequently going beyond the peer-to-peer networking and enabling both horizontal and extended vertical integration of operations. This paper proposes architecture for Production Internet, which is suited to the large scale, dispersion, heterogeneity, and complexity of operations. The mechanisms of coordination rely on externalized governance and are derived from the known theories of dynamic behaviour in networks. By combining ecosystem-wide intelligence with performance measurement, a setup was designed for adaptive control of orders and maintenance of homeostasis, as well as to aid evolution. All these functionalities are embodied into the architecture through a distributed, agent-based and heterarchical solution. The proposed approach aims to improve overall performance, considering the turnover and efficiency of resources, the ‘price of anarchy’, and the Pareto optimality.

Keywords

Production Internet Virtual ecosystems Collaborative networks Multi-agent systems Smart manufacturing Cloud manufacturing 

References

  1. 1.
    Strzelczak, S.: Production Internet - functional perspective. LNCS, vol. 513, pp. 48–56 (2017)Google Scholar
  2. 2.
    Strzelczak, S.: Idiosyncratic behavior of globally distributed manufacturing. LNCS, vol. 398, pp. 487–494 (2012)Google Scholar
  3. 3.
    Geissbauer, R., Schrauf, S., Koch, V., Kuge, S.: Industry 4.0 – Opportunities and Challenges of Industrial Internet, PricewaterhouseCoopers AG, p. 12 (2014). https://www.pwc.nl/en/assets/documents/pwc-industrie-4-0.pdf. Accessed 2 Oct 2017
  4. 4.
    Karmarkar, A., Buchheit, M.: The Industrial Internet of Things, Volume G8: Vocabulary, Industrial Internet Consortium, p. 13 (2017). https://www.iiconsortium.org/pdf/IIC_Vocab_Technical_Report_2.0.pdf. Accessed 2 Oct 2017
  5. 5.
    Strzelczak, S., Berka, A.: Contribution of the theory of parallel computation to the management of distributed manufacturing systems. In: Bin, H., McGeough, J.A., Wu, H. (eds.) Computer-Aided Production Engineering, pp. 29–42. PEP Ltd., London (2001)Google Scholar
  6. 6.
    Strzelczak, S.: Implementing ontologies in manufacturing and logistics – from theoretical fundamentals to prospects. In: Strzelczak, S., Balda, P., Garetti, M., Lobov, A. (eds.) Open Knowledge Driven Manufacturing and Logistics - The eScop Approach, pp. 111–213. OWPW, Warsaw (2015)Google Scholar
  7. 7.
    Strzelczak, S: Operational Risk Management. Warsaw University of Technology Scientific Papers, Series: Organization and Management, No. 21 (2008)Google Scholar
  8. 8.
    Holma, H., Salo, J.: Improving management of supply chains by information technology. In: Waters, D., Rinsler, S. (eds.) Global Logistics – New Directions in Supply Chain Management, pp. 227–243. Kogan Page, London (2015)Google Scholar
  9. 9.
    Shi, X., Chan, S.: Information systems and information technologies for supply chain management. In: Waters, D., Rinsler, S. (eds.) Global Logistics – New Directions in Supply Chain Management, pp. 210–226. Kogan Page, London (2015)Google Scholar
  10. 10.
    He, W., Xu, L.: A state-of-the-art survey of cloud manufacturing. Int. J. Comput. Integr. Manuf. 28(3), 239–250 (2015)CrossRefGoogle Scholar
  11. 11.
    Wu, D., Greer, M.J., Rosen, D.W., Schaefer, D.: Cloud manufacturing: strategic vision and state-of-the-art. J. Manuf. Syst. 32(4), 564–579 (2013)CrossRefGoogle Scholar
  12. 12.
    Vonk, J., Derks, W., Grefen, P., Koetsier, M.: Cross-organizational transaction support for virtual enterprises. LNCS, vol. 1901, pp. 323–334 (2000)Google Scholar
  13. 13.
    Liu, Y., Xu, X., Zhang, L., Wang, L., Zhong, R.Y.: Workload-based multi-task scheduling in cloud manufacturing. Robot. Comput. Integr. Manuf. 45, 3–20 (2017)CrossRefGoogle Scholar
  14. 14.
    Wolf, S., Merz, P.: A hybrid method for solving large-scale supply chain problems. In: Cotta, C., van Hemert, J. (eds.) Evolutionary Computation in Combinatorial Optimization. LNCS, vol. 4446, pp. 219–228. Springer, Berlin (2007)CrossRefGoogle Scholar
  15. 15.
    Dong, J., Zhang, D., Nagurney, A.: A supply chain network equilibrium model with random demands. Eur. J. Oper. Res. 156(1), 194–212 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Mizgier, K.J., Wagner, S.M., Hołyst, J.A.: Modeling defaults of companies in multi-stage supply chain networks. Int. J. Prod. Econ. 135(1), 14–23 (2012)CrossRefGoogle Scholar
  17. 17.
    Little, J.D.C.: Little’s law as viewed on its 50th anniversary. Oper. Res. 59(3), 536–549 (2011)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Kingman, J.F.C.: The single server queue in heavy traffic. Math. Proc. Camb. Philos. Soc. 57(4), 902–904 (1961)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Barrat, A., Barthélemy, M., Vespignani, A.: Dynamical Processes on Complex Networks. Cambridge University Press, Cambridge (2010)zbMATHGoogle Scholar
  20. 20.
    Colombo, A.W., Karnouskos, S., Mendes, J.M., Leitão, P.: Industrial agents in the era of service-oriented architectures and cloud-based industrial infrastructures. In: Leitão, P., Karnouskos, S. (eds.) Industrial Agents: Emerging Applications of Software Agents in Industry, pp. 67–87. Elsevier, Amsterdam (2015)CrossRefGoogle Scholar
  21. 21.
    Zheng, Z., Xie, S., Dai, H., Chen, X, Wang, H.: An overview of blockchain technology: architecture, consensus, and future trends. In: Proceedings of the IEEE International Congress on Big Data, Honolulu, pp. 557–564. IEEE (2017)Google Scholar
  22. 22.
    Marshall, A.: The Unity of Nature: Wholeness and Disintegration in Nature and Science. Imperial College Press, London (2002)CrossRefGoogle Scholar
  23. 23.
    Wiejak-Grądziel, A.: Causal analysis of demand management inaccuracies in manufacturing companies. Master thesis (Supervisor: S. Strzelczak), Warsaw University of Technology, Faculty of Production Engineering (2017)Google Scholar
  24. 24.
    Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M.: Semi-supervised learning with deep generative models. Adv. Neural. Inf. Process. Syst. 27, 3581–3589 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Production EngineeringWarsaw University of TechnologyWarsawPoland

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