Post-Flood Emergency Medical Supply Transportation Problem using Dijkstra's Algorithm inspired Single-Valued Neutrosophic Graphs

Balaji S. M.; Vishvesh Roshan; Sujatha R.; Tetiana Zhyber

doi:10.67334/jemsci11202630

Authors

Balaji S. M. Department of Mathematics, St. Joseph's College of Engineering, OMR, Chennai, Tamil Nadu, India Author https://orcid.org/0000-0002-7590-9855
Vishvesh Roshan Department of Computer Science, St. Joseph's College of Engineering, OMR, Chennai, Tamil Nadu, India Author https://orcid.org/0009-0002-0103-8062
Sujatha R. School of Science and Humanities (Mathematics), Shiv Nadar University, Kalavakkam, Chennai, Tamil Nadu, India Author https://orcid.org/0000-0002-3379-6776
Tetiana Zhyber Department of Finance, Kyiv National Economic University, Kyiv, Ukraine Author https://orcid.org/0000-0002-4557-023X

DOI:

https://doi.org/10.67334/jemsci11202630

Keywords:

SVN-graph, Neutrosophic fuzzy number, Shortest path, Dijkstra’s algorithm, Emergency medical supply

Abstract

In this paper, arc traversal costs are described as Single-Valued Neutrosophic (SVN) Fuzzy Numbers, which concurrently capture acceptance, rejection, and indeterminacy. This provides a useful framework for solving the Shortest-Path Problem (SPP) under extreme uncertainty. The research question is how to efficiently find an economical shortest path in an acyclic directed network with such imprecise weights without requiring prohibitively large computations. Our methodology ensures tractability and resilience by successively evaluating SVN edge lengths using a heuristic ranking function and a modified Dijkstra's algorithm. A real-world case study on the transportation of emergency medical supplies during a flood disaster and a numerical comparison with exhaustive enumeration are used for validation. The results show that the heuristic performs well under highly uncertain disaster conditions and consistently finds near-optimal paths while significantly reducing runtime. These results are important because they provide a deployable, computationally effective neutrosophic SPP solution for time-sensitive logistics, improving crisis response in situations where information is conflicting and incomplete.

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