Advertisement

Intuitionistic Fuzzy Sets for Estimating the Parameters of Distributive Task

  • Alexander BozhenyukEmail author
  • Margarita Knyazeva
  • Olesiya Kosenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

This article proposes an approach to assessing factors that affect the solution of distribution problems. Distribution tasks are widely used at present. The system principle of investigating the objects of the distribution system corresponds to the understanding that when studying them it is necessary to start from internal connections and multilateral interdependencies between a large number of elements. The increase of the system parameters allows to optimize complex resource allocation problems and to take into account a greater number of factors affecting the final result. One of the important parameters of the distribution system is demand. A correct definition of the magnitude of demand affects the solution of several problems: planning and organization of production procedure; calculation of optimal levels of orders for resources, as well as the determination of volumes and the rational functioning of the transport subsystem. Since the total number of factors influencing the level of demand is very high, an expert needs a tool to distinguish groups of such factors. In order to solve this problem it is proposed to use intuitionistic fuzzy sets, which allow to take into consideration the influence degree of factors on the controlled parameter. This approach allows a large number of unordered factors to be converted into a small number of significant and agreed factors, which can provide the basis for a visual and informative analysis.

Keywords

Distribution of resources Fuzzy parameters Factors of influence Intuitionistic fuzzy set Measure of similarity 

Notes

Acknowledgments

This work has been supported by the Russian Foundation for Basic Research, Project № 18-01-00023a.

References

  1. 1.
    Schenk, M., Tolujew, J., Reggelin, T.: A mesoscopic approach to the simulation of logistics systems. In: Advanced Manufacturing and Sustainable Logistics, pp. 15–25. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Giraud, L., Bavière, R., Vallée, M., Paulus, C.: Recent advances in modelling, simulation and operational optimization of DH systems. Euroheat and Power (English Edition) 13(4), 12–15 (2016)Google Scholar
  3. 3.
    Muckstadt, J., Sapra, A.: Principles of Inventory Management. When You Are Down to Four Order More. Springer Series in Operations Research and Financial Engineering. Springer, New York (2010)CrossRefGoogle Scholar
  4. 4.
    Brandimarte, P., Zotteri, G.: Introduction to Distribution Logistics. Wiley, Hoboken (2007)CrossRefGoogle Scholar
  5. 5.
    Gudehus, T., Kotzab, H.: Comprehensive Logistics. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Wardlow, D., Wood, D., Johnson, P.: Modern logistic. Murphy. Trudged., Publ. house Williams (2002)Google Scholar
  7. 7.
    Rushton, A., Croucher, P., Baker, P.: The Handbook of Logistics and Distribution Management: Understanding the Supply Chain. Kogan Page, London (2014)Google Scholar
  8. 8.
    Du, D.-Z., Ko, K.I., Hu, X.: Design and Analysis of Approximation Algorithms. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Kuzmin, E.: Uncertainty and Certainty in Management of Organizational-Economic Systems. LAP LAMBERT Academic Publishing, Munich (2012)Google Scholar
  10. 10.
    Mac Queen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  11. 11.
    Lambert, D., Stock, J., Ellram, L.: Fundamentals of Logistics Management. McGraw-Hill/Irwin, New York (1997)Google Scholar
  12. 12.
    Ross, D.: Introduction to Supply Chain Management Technologies. CRC Press, Boca Raton (2010)Google Scholar
  13. 13.
    Seraya, O.: Mnogomernye modeli logistiki v usloviyah neopredelennosti. FOP Stecenko I. I., Kharkiv (2010)Google Scholar
  14. 14.
    Kosenko, O., Sinyavskaya, E., Shestova, E., Kosenko, E., Chemes, O.: Method for solution of the multi-index transportation problems with fuzzy parameters. In: XIX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 179–182 (2016)Google Scholar
  15. 15.
    Kosenko, O., Shestova, E., Sinyavskaya, E., Kosenko, E., Nomerchuk, A., Bozhenyuk, A.: Development of information support for the rational placement of intermediate distribution centers of fuel and energy resources under conditions of partial uncertainty. In: XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 224–227 (2017)Google Scholar
  16. 16.
    Dubois, D., Prade, H.: Fuzzy Sets and Systems. Academic Press, New York (1980)zbMATHGoogle Scholar
  17. 17.
    Raskin, L., Seraya O.: Nechetkaya matematika. Osnovy teorii. Prilozheniya. Parus, Kharkiv (2008)Google Scholar
  18. 18.
    Atanassov, K.: On Intuitionistic Fuzzy Sets Theory. Springer, New York (2012)CrossRefGoogle Scholar
  19. 19.
    Atanassov, K.: New operations defined over the intuitionistic fuzzy sets. Fuzzy Sets Syst. 61(2), 137–142 (1994)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Shabir, M., Khan, A.: Intuitionistic fuzzy filters of ordered semigroups. J. Appl. Math. Inform. 26(5–6), 213–220 (2008)zbMATHGoogle Scholar
  21. 21.
    Pagurova, V.: A limiting multidimensional distribution of intermediate order statistics. Mosc. Univ. Comput. Math. Cybern. 41(3), 130–133 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander Bozhenyuk
    • 1
  • Margarita Knyazeva
    • 1
  • Olesiya Kosenko
    • 1
  1. 1.Southern Federal UniversityTaganrogRussia

Personalised recommendations