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Decision-Making on Flow Control Under Fuzzy Conditions in the Mechanical Transport System

  • Stanislav Belyakov
  • Marina SavelyevaEmail author
  • Dmitry Kiyashko
  • Anna Lashchenkova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 641)

Abstract

The article deals with the problem of moving flows in mechanical transport systems suitable for prevention or greatly decreasing the probability of emergency situations. The solution is based on minimizing costs during transportation. Routing methods considering the specifics of the MTS are analyzed. It’s developed routing algorithm with protective correction of flows with fuzzy temporal variability of adaptation. The algorithm consists in definition and establishment of high value of transportation cost on the particular segment of network on a fuzzy time interval. Methods for determining the parameters of protective correction of flows are studied. A structural diagram of the MTS, considering the protective correction, is presented. The diagram is implemented by introduction an intelligent module into the structure. Module operation feature is the use of case-based reasoning. The example of the implementation of protective correction of flows is given.

Keywords

Mechanical transport system (MTS) Dynamic routing Adaptive routing Protective correction Case-based reasoning (CBR) 

Notes

Acknowledgments

This work has been supported by supported by the Council for Grants (under RF President) and State Aid of Leading Scientific Schools (Grant MK-521.2017.8).

References

  1. 1.
    Yan, J., Vyatkin, V.: Distributed execution and cyber-physical design of baggage handling automation with IEC 61499. In: 9th International IEEE Conference on Industrial Informatics, INDIN 2011, Lisbon, Portugal, pp. 573–578 (2011)Google Scholar
  2. 2.
    Bellman, R.: On a routing problem. Q. Appl. Math. 16, 87–90 (1958)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Laporte, G.: The vehicle routing problem: an overview of exact and approximate algorithms. Eur. J. Oper. Res. 59(3), 345–358 (1992)CrossRefzbMATHGoogle Scholar
  4. 4.
    Golden, B.L., Raghavan, S., Wasil, E.A. (eds.): The Vehicle Routing Problem: Latest Advances and New Challenges, vol. 43. Springer, Berlin (2008)Google Scholar
  5. 5.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn, p. 1312. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  6. 6.
    Toth, P., Vigo, D. (eds.): Vehicle Routing: Problems, Methods, and Applications, vol. 18. Siam, Philadelphia (2014)Google Scholar
  7. 7.
    Pang, C., Yan, J., Vyatkin, V., Jennings S.: Distributed IEC 61499 material handling control based on time synchronization with IEEE 1588. In: IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication, Munich, pp. 126–131 (2011)Google Scholar
  8. 8.
    Ash, G.R.: Dynamic Routing in Telecommunications Networks, p. 746. McGraw-Hill, New York (1997)Google Scholar
  9. 9.
    Kodialam, M., Lakshman, T. V.: Dynamic routing of bandwidth guaranteed tunnels with restoration. In: Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Proceedings, INFOCOM 2000, vol. 2, pp. 902–911. IEEE (2000)Google Scholar
  10. 10.
    Kleinrock, L., Gail, R.: Queueing Systems: Problems and Solutions, p. 240. Wiley, New York (1996)zbMATHGoogle Scholar
  11. 11.
    Guizani, M., Rayes, A., Khan, B., Al-Fuqaha, A.: Network Modeling and Simulation: A Practical Perspective, p. 304. Wiley, Hoboken (2010)CrossRefGoogle Scholar
  12. 12.
    Mak, T., Cheung, P.Y.K., Lam, K.-P., Luk, W.: Adaptive routing in network-on-chips using a dynamic-programming network. IEEE Trans. Ind. Electron. 58(8), 3701–3716 (2011)CrossRefGoogle Scholar
  13. 13.
    Aksaray, D., Vasile, C.I., Belta C.: Dynamic routing of energy-aware vehicles with temporal logic constraints. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3141–3146 (2016)Google Scholar
  14. 14.
    Belyakov, S., Savelyeva, M., Yan, J., Vyatkin, V.: Adaptation of material flows in mechanical transportation systems based on observation experience. In: 2015 IEEE Trustcom/BigDataSE/ISPA, vol. 3, pp. 269–274 (2015)Google Scholar
  15. 15.
    Savelyeva, M.: The construction of the fuzzy route based on case-based reasoning. In: Belyakov, S., Rozenberg, I., Savelyeva. M. (eds.) Proceedings of 19th International Conference on Soft Computing MENDEL 2013, pp. 273–276 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Stanislav Belyakov
    • 1
  • Marina Savelyeva
    • 1
    Email author
  • Dmitry Kiyashko
    • 1
  • Anna Lashchenkova
    • 1
  1. 1.Southern Federal UniversityTaganrogRussia

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