A Case Study on Applying Equally and Mildly Balanced Neutrosophic Graphs to Optimize Urban Transportation Management

  • Authors

    • Balaji S M Department of Mathematics, St. Joseph's College of Engineering, OMR, Chennai, 600119, Tamil Nadu, India
    • Meiyappan D Department of Mathematics, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai, 602117, Tamil Nadu, India
    • Sujatha R School of Science and Humanities (Mathematics), Shiv Nadar University, Kalavakkam, Chennai, 603110, Tamil Nadu, India
    • Shoba B Department of Mathematics, St. Joseph's College of Engineering, OMR, Chennai, 600119, Tamil Nadu, India
    https://doi.org/10.14419/ehwap760

    Received date: July 18, 2025

    Accepted date: September 12, 2025

    Published date: September 21, 2025

  • Density of SVN-graph; EB SVN-subgraphs; Feeble-subgraph; Intense-subgraph; MB SVN-subgraphs; WB SVN-subgraphs
  • Abstract

    A flexible approach to interpreting balancedness in neutrosophic graphs is provided by the concept of a mild balanced single-valued neutrosophic (MB SVN) graph, which strives for an equitable representation while acknowledging the inherent inconsistencies in real-world data. Although reaching a condition of equal balance in Single Valued Neutrosophic (EB SVN) graphs would be ideal, in practice, the complexity and uncertainty of real-world data can pose difficulties for their application.  In this research, we analyze subgraphs with varying densities, distinguishing between intense and feeble ones. Additionally, we discuss the characteristics of mild balanced SVN (MB SVN) graphs, wimpy balanced SVN (WB SVN) graphs, and equally balanced SVN (EB SVN) graphs. Analyzing the union and sum processes used to SVN-graphs is the main emphasis of our work. We have constructed an algorithm to find every balanced SVN-graph. Moreover, we have investigated how the traffic management system might benefit from MB SVN-subgraphs, WB SVN-subgraphs, and EB SVN-subgraphs. We have also thought about the possible application of mild balanced graphs in traffic control systems and route planning.

  • References

    1. Ahuja R K, Magnanti T L, Orlin J B & Reddy M (1995), Applications of network optimization. Handbooks in Operations Research and Manage-ment, Science 7, 1–83.
    2. Akram M (2016), Single-valued neutrosophic planar graphs. International Journal of Algebra and Statistics 5(2), 157–167.
    3. Akram M & Shahzadi G (2017), Operations on single-valued neutrosophic graphs. Infinite Study.
    4. Akram M & Shahzadi S (2016), Representation of graphs using intuitionistic neutrosophic soft sets. Infinite Study.
    5. Akram M & Shahzadi S (2017), Neutrosophic soft graphs with application. Journal of Intelligent & Fuzzy Systems, 32(1), 841-858.
    6. Akram M & Waseem N (2018), Novel applications of bipolar fuzzy graphs to decision making problems. Journal of Applied Mathematics and Computing, 56, 73–91.
    7. Akram M, Karunambigai M, Palanivel K & Sivasankar S (2014), Balanced bipolar fuzzy graphs. Journal of advanced research in pure mathematics, 6(4), 58–71.
    8. Akram, M., Ashraf, A., & Sarwar, M. (2014). Novel applications of intuitionistic fuzzy digraphs in decision support systems. The Scientific World Journal, 2014(1), 904606.
    9. Al-Hawary T (2011), Complete fuzzy graphs. International Journal of Mathematical Combinatorics, 4, p.26.
    10. Amanathulla S, Bera B & Pal M (2021), Balanced picture fuzzy graph with application. Artificial Intelligence Review 54(7), 5255–5281.
    11. Atanasov K (1986), Intuitionistic fuzzy sets.fuzzy sets and systems.
    12. Atanassov K T (1983), Intuitionistic fuzzy sets vii itkr’s session. Sofia, June 1, 983.
    13. Balaji S M, Meiyappan D & Sujatha R (2024), Analyzing single-valued neutrosophic fuzzy graphs through matroid perspectives. Ain Shams Engi-neering Journal, 103133.
    14. Broumi S, Sivasankar S, Bakali A & Talea M (2024), Balanced neutrosophic fermatean graphs with applications. In: Analytical Decision Making and Data Envelopment Analysis: Advances and Challenges, pp. 413–431. Springer.
    15. Broumi S, Smarandache F, Talea M & Bakali A (2016), Single valued neutrosophic graphs: degree, order and size. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 2444–2451. IEEE.
    16. Chen F H, Shen C & Shi F G (2018), A new approach to the fuzzification of arity, jhc and cup of l-convexities. Journal of Intelligent & Fuzzy Sys-tems, 34(1), 221–231.
    17. Devi R N, Kalaivani N, Broumi S & Venkatesan K (2018), Characterizations of Strong and Balanced Neutrosophic Complex Graphs. Infinite Study.
    18. Karunambigai M, Sivasankar S & Palanivel K (2014), Properties of balanced intuitionistic fuzzy graphs. International Journal of Research in Since 1, 01–05.
    19. Li Y, Li J, Duan H & Qiu D (2019), A generalization of gv fuzzy matroids based on intuitionistic fuzzy sets. Journal of Intelligent & Fuzzy Sys-tems, 37(4), 5049–5060.
    20. Mahapatra R, Samanta S & Pal M (2021), Generalized neutrosophic planar graphs and its application. Journal of Applied Mathematics and Compu-ting, 65(1-2), 693–712.
    21. Malarvizhi J, Gnanajeya T & Geetha T (2021), Isomorphic single valued neutrosophic graphs and their complements. Adv Appl Math Sci, 20(8), 1375-1388.
    22. Nivethana V & Parvathi A (2017), Mild balanced intuitionistic fuzzy graphs, Int. Journal of Engineering Research and Application, 7, 13–20.
    23. Nivethana V & Parvathi A (2015), On complement of intuitionistic fuzzy graphs. International journal of computational and applied mathematics, 10(1), 17–26.
    24. Sankar C, Kalaivani C, Chellamani P & Venkat Narayanan G (2025), Analyzing network stability via symmetric structures and domination integrity in signed fuzzy graphs. Symmetry 17(5), 766.
    25. Sankar C, Kalaivani C, Saravanan M & Sujatha R (2025), An algorithmic approach to signed fuzzy graph integrity: Complexity, graph operations, and metro rail network applications. Ain Shams Engineering Journal,16(9), 103509.
    26. Sivasankar S & Broumi S (2022), Balanced neutrosophic graphs. Neutrosophic Sets and Systems, 50, 309–319.
    27. Smarandache F (1999), A unifying field in logics. neutrosophy: Neutrosophic probability, set and logic. American Research Press, Rehoboth.
    28. Smarandache F, Vasantha Kandasamy W & Ilanthenral K (2015), Neutrosophic graphs: A new dimension to graph theory.
    29. Thilagavathi S, Parvathi R, & Karunambigai M G (2009), Operations on intuitionistic fuzzy graphs II. In Conf. Pap. Int. J. Comput. Appl (Vol. 5, pp. 89-113).
    30. Wang H, Smarandache F, Zhang Y & Sunderraman R (2010), Single valued neutrosophic sets. multispace and multistructure, 4, 410–413.
    31. Yang H L, Guo Z L, She Y & Liao X (2016), On single valued neutrosophic relations. Journal of Intelligent & Fuzzy Systems 30(2), 1045–1056.
    32. Ye J (2014), A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. Journal of Intelligent & Fuzzy Systems, 26(5), 2459–2466.
    33. Ye S & Ye J (2014), Dice similarity measure between single valued neutrosophic multisets and its application in medical diagnosis. Neutrosophic sets and systems 6(1), 9.
    34. Zadeh L A (1965), Fuzzy sets, information and control 8 (3): 338–353.
  • Downloads

  • How to Cite

    S M, B., D, M. ., R, S. ., & B, S. . (2025). A Case Study on Applying Equally and Mildly Balanced Neutrosophic Graphs to Optimize Urban Transportation Management. International Journal of Basic and Applied Sciences, 14(5), 782-789. https://doi.org/10.14419/ehwap760