USING FUZZY GRAPH INVARIANTS FOR THE STABILITY ANALYSIS OF COMPLEX TRANSPORT SYSTEMS

Abstract

This article examines the issues of assessing the sustainability of transport and logistics systems (TLS) under conditions of uncertainty, which play a key role in ensuring the effective functioning of supply chains. The sustainability of systems is analyzed in the context of their ability to adapt to external and internal influences, such as economic fluctuations, changes in demand, natural disasters and technological failures. In this paper, it is proposed to use fuzzy graph invariants, namely, a fuzzy dominating set, to assess and analyze the sustainability of transport and logistics systems under uncertainty. It is shown that a fuzzy dominating set allows solving the problem of placing distribution hubs in a transport and logistics system. Examples of finding fuzzy dominating sets for fuzzy and fuzzy temporal graphs as the models of transport and logistic system are presented. Fuzzy temporal graphs also allow for more adequate modeling and analysis of systems in cases where the time parameter is one of the important factors. The practical significance of the study lies in the possibility of designing a more reliable and adaptive TLS capable of functioning effectively under conditions of uncertainty. The results can be used to optimize logistics processes, reduce costs and increase the sustainability of supply chains. The findings also open prospects for further research in the field of integrating artificial intelligence methods and big data analysis in transport system management. Further research is proposed to be directed at integrating flow optimization methods considering time factors and developing digital twins of TLS.

Authors

References

1. Cai J., Liu X., Xiao Z., Liu J. Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment, Decision Support Systems, 2009, 46 (2), pp. 512-521.

2. Izvarina N., Vorozhbit L., etc. Problems of the economy and economic security in the Russian Federa-tion in the context of digitalization, Essence, role and the place of economic security in the system of the national security, 2019, No. 4, pp. 47-60.

3. Kondratenko Y., Kondratenko G., Sidenko Ie., Taranov M. Fuzzy and Evolutionary Algorithms for Transport Logistics Under Uncertainty, Intelligent and Fuzzy Techniques: Smart and Innovative Solu-tion, 2021, pp.1456-1463. – DOI:10.1007/978-3-030-51156-2_169.

4. Kiselenko A.N., Sundukov E. Yu, Tarabukina N.A. World of Transport and Transportation, Methods to Forecast Transport Systems Development under Modern Conditions, 2022, Vol. 20, Issue 3 (100),

p. 158-167.

5. Scipioni A., Manzardo A., Ren J. Hydrogen Economy. Supply Chain, Life Cycle Analysis and Energy Transition for Sustainability. Academic Press, 2017, 328 p.

6. Cinar D., Gakis K., Pardalos P. Sustainable Logistics and Transportation: Optimization Models and Algorithms. Springer. 2017, 338 p.

7. Xiugang W., Lysochenko A. A. Identification of Factors Influencing the Construction of Supply Chains in China’s Transport and Logistics Systems, Management Sciences, 2024,Vol. 13, pp. 428-439.

8. Osintsev N., Rakhmangulov A. Supply Chain Sustainability Drivers: Identification and Multi-Criteria Assessment, Logistics, 2025, Vol. 9, No. 1, pp. 24.

9. Karim M.R., Dulal M., Sakila F., Aditi P., Smrity S.J., Asha N.N. Analyzing the factors influencing sus-tainable supply chain management in the textile sector, Cleaner Logistics and Supply Chain, 2024, Vol. 13, pp. 101.

10. Kanchana M., Kavitha K. A review on transportation and smart logistics using graph theoretical ap-proach, Advances in Mathematics: Scientific Journal, 2020, 32, pp. 612-635.

11. Yatskin D.V., Kochkarov A.A., Kochkarov R.A. Modeling of transport and logistics systems and the study of the structural stability, Management Sciences in Russia, 2020, pp. 102-111.

12. Chislov O., Lyabakh N., Kolesnikov M., Bakalov M., Bezusov D. Fuzzy modelling of the transportation logistics processes, Journal of Physics: Conference Series, 2021, 14 (2), pp. 77-87.

13. Wang G. TOPSIS Evaluation System of Logistics Transportation Based on an Ordered Representation of the Polygonal Fuzzy Set, International Journal of Fuzzy Systems, 2020, Vol. 22, No. 5, pp. 1565-1581.

14. Dey B. Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria, International Journal of Management Science and Engineering Man-agement, 2015, Vol. 11, No. 4, pp. 262-278.

15. Ore O. Theory of graphs. Amer. Math. Soc. Colloq. Publ. Providence, 1962.

16. Mordeson J.N. Nair P.S. Fuzzy Graphs and Fuzzy Hypergraphs, Studies in Fuzziness and Soft Comput-ting, 2000, 46.

17. Kaufmann A. Introduction a la theorie des sous-ensemles flous. Paris: Masson, 1977.

18. Grötschel M., Lovász L., Schrijver A. Stable Sets in Graphs, Algorithms and Combinatorics, 1993, 2, pp. 272-303.

19. Bershteyn L.S., Bozhenyuk A.V. Nechetkie grafy i gipergrafy [Fuzzy graphs and hypergraphs]. Moscow: Nauchnyy mir, 2005, 255 p.

20. Nagoorgani A., Chandrasekaran V.T. Domination in fuzzy graph, Advances in Fuzzy Sets and Systems, 2006, 1 (1), pp. 17-26.

21. Varajão J., Lourenço J.C., Gomes J. Models and methods for information systems project success eval-uation, A review and directions for research, 2022, 8 (12), e11977.

22. Bozhenyuk A., Belyakov S., Knyazeva M., Rozenberg I. Searching Method of Fuzzy Internal Stable Set as Fuzzy Temporal Graph Invariant, Communications in Computer and Information Science, 2018, 583, pp. 501-510.

Скачивания

Published:

2025-12-30

Issue:

Section:

SECTION II. DATA ANALYSIS, MODELING AND CONTROL

Keywords:

Transport and logistics system (TLS), uncertainty, system stability, fuzzy graph, fuzzy temporal graph, fuzzy domination set, dynamic parameters

For citation:

I.N. Rozenberg , I.А. Dubchak USING FUZZY GRAPH INVARIANTS FOR THE STABILITY ANALYSIS OF COMPLEX TRANSPORT SYSTEMS. IZVESTIYA SFedU. ENGINEERING SCIENCES – 2025. - № 6. – P. 136-145.