A Heuristic-Accelerated Yen’s Algorithm for K-Shortest Time-Dependent Routing under a Fermatean Neutrosophic Framework

Abstract

Time dependent path computation and alternative route selection have become an increasingly 
important area of research in intelligent transportation systems, logistics planning and network 
optimization. In this paper, we investigate yen’s K-Shortest time dependent path problem in 
networks represented by fermatean neutrosophic information. We use time varying 
membership, in determination and non-membership functions to represent edge weights. In 
order to overcome the limitations of classical shortest path algorithms that cannot handle 
uncertainty and temporal variability simultaneously, we propose a heuristic-guided framework 
for efficient computation of yen’s K-Shortest paths. An analysis of the computational 
complexity indicates a significant improvement of the practical performance of the proposed 
method when compared with conventional methods for computing K-Shortest paths. 

DOI 10.5281/zenodo.21957897