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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
Brandimarte, P., Zotteri, G.: Introduction to Distribution Logistics. Wiley, Hoboken (2007)
Gudehus, T., Kotzab, H.: Comprehensive Logistics. Springer, Heidelberg (2012)
Wardlow, D., Wood, D., Johnson, P.: Modern logistic. Murphy. Trudged., Publ. house Williams (2002)
Rushton, A., Croucher, P., Baker, P.: The Handbook of Logistics and Distribution Management: Understanding the Supply Chain. Kogan Page, London (2014)
Du, D.-Z., Ko, K.I., Hu, X.: Design and Analysis of Approximation Algorithms. Springer, Heidelberg (2012)
Kuzmin, E.: Uncertainty and Certainty in Management of Organizational-Economic Systems. LAP LAMBERT Academic Publishing, Munich (2012)
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)
Lambert, D., Stock, J., Ellram, L.: Fundamentals of Logistics Management. McGraw-Hill/Irwin, New York (1997)
Ross, D.: Introduction to Supply Chain Management Technologies. CRC Press, Boca Raton (2010)
Seraya, O.: Mnogomernye modeli logistiki v usloviyah neopredelennosti. FOP Stecenko I. I., Kharkiv (2010)
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)
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)
Dubois, D., Prade, H.: Fuzzy Sets and Systems. Academic Press, New York (1980)
Raskin, L., Seraya O.: Nechetkaya matematika. Osnovy teorii. Prilozheniya. Parus, Kharkiv (2008)
Atanassov, K.: On Intuitionistic Fuzzy Sets Theory. Springer, New York (2012)
Atanassov, K.: New operations defined over the intuitionistic fuzzy sets. Fuzzy Sets Syst. 61(2), 137–142 (1994)
Shabir, M., Khan, A.: Intuitionistic fuzzy filters of ordered semigroups. J. Appl. Math. Inform. 26(5–6), 213–220 (2008)
Pagurova, V.: A limiting multidimensional distribution of intermediate order statistics. Mosc. Univ. Comput. Math. Cybern. 41(3), 130–133 (2017)
Acknowledgments
This work has been supported by the Russian Foundation for Basic Research, Project № 18-01-00023a.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bozhenyuk, A., Knyazeva, M., Kosenko, O. (2019). Intuitionistic Fuzzy Sets for Estimating the Parameters of Distributive Task. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-04164-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04163-2
Online ISBN: 978-3-030-04164-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)