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Models Adaptation of Complex Objects Structure Dynamics Control

  • Boris V. Sokolov
  • Vyacheslav A. Zelentsov
  • Olga Brovkina
  • Victor F. Mochalov
  • Semyon A. Potryasaev
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 348)

Abstract

In this paper we present a dynamic multiple criteria model of integrated adaptive planning and scheduling for complex objects (CO). Various types of CO are in use currently, for example: virtual enterprises, supply chains, telecommunication systems, etc. Hereafter, we refer to CO as systems of those types. The adaptation control loops are explicitly integrated within the model of analytical simulation. The mathematical approach is based on a combined application of control theory, operations research, systems analysis, and modeling and simulation theory. In particular, a scheduling problem for CO is considered as a dynamic interpretation. New procedures of dynamic decomposition help us to find the parameter values of the model’s adaptation. The example demonstrates a general optimization scheme to be applied to the problem of division of competencies between the coordinating and operating levels of the CO via parametric adaptation of the model’s described structure dynamics control processes.

Keywords

complex technical - organizational system structure dynamic control planning and scheduling parametric and structure adaptation of models 

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References

  1. 1.
    Ohtilev, M.Y., Sokolov, B.V., Yusupov, R.M.: Intellectual Technologies for Monitoring and Control of Structure dynamics of Complex Technical Objects, 410 p. Moscow, Nauka (2006) (in Russian)Google Scholar
  2. 2.
    Zaychik, E., Sokolov, B., Verzilin, D.: Integrated modeling of structure dynamics control in complex technical systems. In: 19th European Conference on Modeling and Simulation ESMS 2005, “Simulation in Wider Europe”, June 1-4, pp. 341–346. Riga Technical University, Riga (2005)Google Scholar
  3. 3.
    Ivanov, D., Sokolov, B., Arkhipov, A.: Stability analysis in the Framework of decision Making under Risk and Uncertainty Network – Centric Collaboration and Supporting Frameworks. In: Camarinha-Matos, L., Afsarmanesh, H., OUus, M. (eds.) Network – Centric Collaboration and Supporting Frameworks, IFIP TC5WG 5.5 Seventh IFIP Working Conference on Virtual Enterprises, Helsinki, Finland, September 25-27. IFIP, vol. 224, pp. 211–218. Springer, Boston (2006)CrossRefGoogle Scholar
  4. 4.
    Skurihin, V.I., Zabrodsky, V.A., Kopeychenko, Y.V.: Adaptive control systems in machine-building industry. Mashinostroenie, – M. (1989)Google Scholar
  5. 5.
    Rastrigin, L.A.: Modern principles of control for complicated objects. Sovetscoe Radio, – M. (1980) (in Russian)Google Scholar
  6. 6.
    Bellmann, R.: Adaptive Control Processes: A Guided Tour. Princeton Univ. Press, Princeton (1972)Google Scholar
  7. 7.
    Rastrigin, L.A.: Adaptation of complex systems. Zinatne, Riga (1981) (in Russian)MATHGoogle Scholar
  8. 8.
    Fleming, W.H., Richel, R.W.: Deterministic and stochastic optimal control. Springer, New York (1975)CrossRefMATHGoogle Scholar
  9. 9.
    Moiseev, N.N.: Element of the Optimal Systems Theory. Nauka, – M. (1974) (in Russian)Google Scholar
  10. 10.
    Sowa, J.: Architecture for intelligent system. IBM System Journal 41(3) (2002)Google Scholar
  11. 11.
    Zypkin Ya. Z. Adaptation and teachning in automatic systems. Nauka, – M. (1969) (in Russian)Google Scholar
  12. 12.
    Bryson, A.E., Ho, Y.-C.: Applied optimal control: Optimization, Estimation and Control. Waltham Massachusetts, Toronto, London (1969)Google Scholar
  13. 13.
    Chernousko, F.L., Zak, V.L.: On Differential Games of Evasion from Many Pursuers. J. Optimiz. Theory and Appl. 46(4), 461–470 (1985)CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Singh, M., Titli, A.: Systems: Decomposition, Optimization and Control. Pergamon Press, Oxford (1978)Google Scholar
  15. 15.
    Petrosjan, L.A., Zenkevich, N.A.: Game Theory. World Scientific Publ., Singapore (1996)CrossRefGoogle Scholar
  16. 16.
    Roy, B.: Multi-criteria Methodology for Decision Aiding. Kluwer Academic Pulisher, Dordrecht (1996)CrossRefGoogle Scholar
  17. 17.
    Fischer, M., Jaehn, H., Teich, T.: Optimizing the selection of partners in production networks. Robotics and Computer-Integrated Manufacturing 20, 593–601 (2004)CrossRefGoogle Scholar
  18. 18.
    Huang, G., Zhang, Y., Liang, L.: Towards integrated optimal configuration of platform products, manufacturing processes, and supply chains. Journal of Operations Management 23, 267–290 (2005)CrossRefGoogle Scholar
  19. 19.
    Kuehnle, H.: A system of models contribution to production network (PN) theory. Journal of Intelligent Manufacturing, 157–162 (2007)Google Scholar
  20. 20.
    Nilsson, F., Darley, V.: On complex adaptive systems and agent-based modeling for improving decision-making in manufacturing and logistics settings. Int. Journal of Operations and Production Management 26(12), 1351–1373 (2006)CrossRefGoogle Scholar
  21. 21.
    Rabelo, R.J., Klen, A.A.P., Klen, E.R.: Multi-agent system for smart coordination of dynamic supply chains. In: Proceedings of the 3rd International Conference on Virtual Enterprises, PRO-VE, pp. 379–387 (2002)Google Scholar
  22. 22.
    Teich, T.: Extended Value Chain Management (EVCM). GUC-Verlag, Chemnitz (2003)Google Scholar
  23. 23.
    Wu, N., Mao, N., Qian, Y.: An approach to partner selection in agile manufacturing. Journal of Intelligent Manufacturing 10(6), 519–529 (1999)CrossRefGoogle Scholar
  24. 24.
    Wu, N., Su, P.: Selection of partners in virtual enterprise paradigm. Robotics and Computer-Integrated Manufacturing 21, 119–131 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Boris V. Sokolov
    • 1
    • 3
  • Vyacheslav A. Zelentsov
    • 1
  • Olga Brovkina
    • 2
    • 4
  • Victor F. Mochalov
    • 1
  • Semyon A. Potryasaev
    • 1
  1. 1.Saint Petersburg Institute of Informatics and Automation (SPIIRAS)Russian Academy of ScienceSt. PetersburgRussia
  2. 2.Global Change Research Centre Academy of Science of the Czech RepublicBrnoCzech Republic
  3. 3.University ITMOSt. PetersburgRussia
  4. 4.Mendel University in BrnoBrnoCzech Republic

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