Advertisement

RFID Technology for Adaptation of Complex Systems Scheduling and Execution Control Models

  • Boris SokolovEmail author
  • Karim Benyamna
  • Oleg Korolev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)

Abstract

In this paper, we investigate the issues of establishing adaptive feedbacks between complex systems (CSs) scheduling and execution from the perspectives of modern control theory. In using optimum control for the scheduling stage, feedback adaptive control for the execution stage, and attainable sets for the analysis of the achievement of the planned performance in a real execution environment, we provide a mathematically unified framework for CSs scheduling and execution control. The proposed framework makes it possible to analyze the correspondence of RFID (Radio Frequency Identification) functionalities and costs to the actual needs of execution control and support problem-oriented CSs adaptation for the achievement of the desired performance. The developed framework can be applied as an analysis tool for the decision support regarding the designing and applying RFID infrastructures in supply chains.

Keywords

Complex systems Scheduling and planning RFID technologies Integrated modeling Multi-agents modeling 

Notes

Acknowledgements

The research is supported by Russian Science Foundation (Project No. 16-19-00199).

References

  1. 1.
    Ohtilev, M.Yu., Sokolov, B.V., Yusupov, R.M.: Intellectual Technologies for Monitoring and Control of Structure-Dynamics of Complex Technical Objects, p. 410. Nauka, Moscow (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”, 1–4 June 2005, pp. 341–346. Riga Technical University, Riga, Latvia (2005)Google Scholar
  3. 3.
    Ivanov, D., Sokolov, B., Arkhipov, A.: Stability analysis in the framework of decision making under risk and uncertainty. In: Camarinha-Matos, L.M., Afsarmanesh, H., Ollus, M. (eds.) Network—Centric Collaboration and Supporting Frameworks, IFIP TC5WG 5.5 Seventh IFIP Working Conference on Virtual Enterprises, 25–27 Sept 2006, pp. 211–218. Springer, Helsinki, Finland (2006)Google Scholar
  4. 4.
    Skurihin, V.I., Zabrodsky, V.A., Kopeychenko, Yu.V.: Adaptive Control Systems In Machine-Building Industry. Mashinostroenie (1989) (in Russian)Google Scholar
  5. 5.
    Rastrigin, L.A.: Modern Principles of Control for Complicated Objects. Sovetscoe Radio (1980) (in Russian)Google Scholar
  6. 6.
    Bellmann, R.: Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton, New Jersey (1972)Google Scholar
  7. 7.
    Rastrigin L.A.: Adaptation of complex systems. Zinatne, Riga (1981) (in Russian)Google Scholar
  8. 8.
    Fleming, W.H., Richel, R.W.: Deterministic and Stochastic Optimal Control. Springer, Berlin (1975)CrossRefGoogle Scholar
  9. 9.
    Moiseev, N.N.: Element of the Optimal Systems Theory. Nauka (1974) (in Russian)Google Scholar
  10. 10.
    Sowa, J.: Architecture for intelligent system. IBM Syst. J. 41(3) (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Zypkin, Ya.Z.: Adaptation and Teachning in Automatic Systems. Nauka (1969) (in Russian)Google Scholar
  12. 12.
    Bryson, A.E., Ho, Yo-Chi: Applied Optimal Control: Optimization Estimation and Control. Waltham, Massachusetts (1969)Google Scholar
  13. 13.
    Singh, M., Titli, A.: Systems: Decomposition, Optimization and Control. Pergamon Press, Oxford (1978)zbMATHGoogle Scholar
  14. 14.
    Petrosjan, L.A., Zenkevich, N.A.: Game Theory. World Scientific Publications, Singapore (1996)CrossRefGoogle Scholar
  15. 15.
    Roy, B.: Multi-criteria Methodology for Decision Aiding. Kluwer Academic Pulisher, Dordreeht (1996)CrossRefGoogle Scholar
  16. 16.
    Nilsson, F., Darley, V.: On complex adaptive systems and agent-based modeling for improving decision-making in manufacturing and logistics settings. Int. J. Oper. Prod. Manag. 26(12), 1351–1373 (2006)CrossRefGoogle Scholar
  17. 17.
    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’2002. pp. 379–387 (2002)Google Scholar
  18. 18.
    Wu, N., Su, P.: Selection of partners in virtual enterprise paradigm. Robot. Comput.-Integr. Manuf. 21, 119–131 (2005)CrossRefGoogle Scholar
  19. 19.
    Angeles, R.: RFID technologies: supply chain applications and implementation issues. Inf. Syst. Manag. (Winter), 51–65 (2005)CrossRefGoogle Scholar
  20. 20.
    Chalasani, S., Boppana, R.V.: Data architectures for RFID transactions. IEEE Trans. Ind. Inf. 3(3), 246–257 (2007)CrossRefGoogle Scholar
  21. 21.
    Huber, S., Michael, K., McCathie, L.: Barriers to RFID adoption in the supply chain Barriers to RFID adoption in the supply chain. In: IEEE RFID Eurasia, pp. 1–6. 5–6 September, Istanbul, Turkey (2007)Google Scholar
  22. 22.
    Henseler, M., Rossberg, M., Schaefer, G.: Credential management for automatic identification solutions in supply chain management. IEEE Trans. Ind. Inf. 4(4), 303–314 (2008)CrossRefGoogle Scholar
  23. 23.
    Rong, C., Cayirci, E.: RFID security. Computer and Information Security Handbook, pp. 205–221 (2009)CrossRefGoogle Scholar
  24. 24.
    Lee, H., Oezer, Oe.: Unlocking the value of RFID. Prod. Oper. Manag. 16(1), 40–64 (2007)CrossRefGoogle Scholar
  25. 25.
    Chuang, M.L., Shaw, W.H.: RFID: integration stages in supply chain management. IEEE Eng. Manag. Rev. 35(2), 80–87 (2007)CrossRefGoogle Scholar
  26. 26.
    Li, S., Visich, J.K.: Radio frequency identification: supply chain impact and implementations challenges. Int. J. Int. Supply Manag. 2(4), 407–424 (2006)CrossRefGoogle Scholar
  27. 27.
    Dashevsky, V., Sokolov, B. (2010). New concept of RFID reader networks structure: hardware and software architecture. In: Proceedings of International Conference on Ultra Modern Telecommunications ICUMT-2009, Saint-Petersburg, RussiaGoogle Scholar
  28. 28.
    Bhardwaj, A., Singh, V.K., Kumar, P.: Multi-agent based train passing in railway system with minimum system delay. In: 2014 IEEE International Advance Computing Conference (IACC) (2014)Google Scholar
  29. 29.
    Niazi, M., Hussain, A.: Agent-based computing from multi-agent systems to agent-based Models: a visual survey. Scientometrics (2011)Google Scholar
  30. 30.
    Müller, J.P.: Des systèmes autonomes aux systèmes multi-agents: Interaction, émergence et systems complexes. Mémoire d’habilitation (2002)Google Scholar
  31. 31.
    Kurve, A., Kotobi, K., Kesidis, G.: An agent-based framework for performance modeling of an optimistic parallel discrete event simulator. Complex Adapt. Syst. Model. 1, 12 (2013). doi: 10.1186/2194-3206-1-12CrossRefGoogle Scholar
  32. 32.
    Salamon, T.: Design of Agent-Based Models. Repin: Bruckner Publishing. p. 22 (2011). ISBN 978-80-904661-1-1Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO)St. PetersburgRussia
  2. 2.St. Petersburg Institute of Informatics and Automation, Russian Academy of Sciences (SPIIRAS)St. PetersburgRussia

Personalised recommendations