Multidimensional Linguistic Variables and Their Application for Resolving the Tasks of Marshaling Processes Automation

  • Alexandr N. Shabelnikov
  • Nikolai N. LyabakhEmail author
  • Natalia A. Malishevskaya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)


Role of multi-dimensional fuzzy sets is updated in the task of railway wagons marshaling processes automation, expanding the resolved tasks range and increasing their solution accuracy. Two identification methods of multi-dimensional functions in tabular form, using univariate membership functions, are developed. Particularly, it is a synthesis based on the operation of fuzzy sets intersection, and a sequential synthesis algorithm for membership functions. An approximation procedure is developed for multi-dimensional membership functions in the points not belonging to table nodes.


Automation of marshaling processes Fuzzy sets Multi-dimensional linguistic variables Operations with fuzzy sets Approximation 


  1. 1.
    Shabelnikov, A.N., Lyabakh, N.N., Ivanchenko, V.N., Sokolov, V.N., Odikadze, V.R., Kovalev, S.M., Sachko, V.I.: Sorting hump automation systems on the basis of modern computer technologies. NIIAS, RGUPS. Rostov-on-Don (2010)Google Scholar
  2. 2.
    Simić, D., et al.: 50 years of fuzzy set theory and models for supplier assessment and selection: a literature review. J. Appl. Logic (2016)Google Scholar
  3. 3.
    Shabelnikov, A.N., Sokolov, V.N.: The latest technologies of marshalling stations automation. Autom. Commun. Inform. № 11 (2007)Google Scholar
  4. 4.
    Lyabakh N.N., Shabelnikov A.N.: Technical cybernetics on railway transport. RGUPS, NCSC HS, Rostov-on-Don (2002)Google Scholar
  5. 5.
    Bray, S., et al.: Measuring transport systems efficiency under uncertainty by fuzzy sets theory based Data Envelopment Analysis: theoretical and practical comparison with traditional DEA model. Transp. Res. Procedia 5, 186–200 (2015)CrossRefGoogle Scholar
  6. 6.
    Adadurov, S.E., Gapanovich, V.A., Ljabah, N.N., Shabelnikov, A.N.: Railway transport: on the way to intelligent management. SSC RAS, Rostov-on-Don (2010)Google Scholar
  7. 7.
    Stepanov, M.F., Stepanov, A.M.: Mathematical modeling of intellectual self-organizing automatic control system: action planning research. In: 3rd International Conference « Information Technology and Nanotechnology » , ITNT-2017, Samara, Russia (2017)CrossRefGoogle Scholar
  8. 8.
    Pupkov, K.A.: Intelligent systems: development and issues. In: XIIth International Symposium « Intelligent Systems » , INTELS 2016, 5–7 October 2016, Moscow, Russia. Procedia Comput. Sci. 103, 581–583 (2017)CrossRefGoogle Scholar
  9. 9.
    Sumalee, A., Ho, H.W.: Smarter and more connected: future intelligent transportation system. IATSS Res. (2017)Google Scholar
  10. 10.
    Finaev, V.I.: Models of Decision-Making Systems. TRTU Publishing, Taganrog (2005)Google Scholar
  11. 11.
    Rosa, M.R., Labella, A., Martínez, L.: An overview on fuzzy modelling of complex linguistic preferences in decision making. Int. J. Comput. Intell. Syst. 9(sup1), 81–94 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexandr N. Shabelnikov
    • 1
    • 2
  • Nikolai N. Lyabakh
    • 1
    • 2
    Email author
  • Natalia A. Malishevskaya
    • 1
  1. 1.Rostov State Transport UniversityRostov-on-DonRussia
  2. 2.JSC «NIIAS»Rostov-on-DonRussia

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