A Two-Stage Genetic Algorithm for Solving Shortest Path Problem with Fuzzy Arc Lengths

This paper investigates solving the shortest path problem with fuzzy arc lengths using a two-stage genetic algorithm (GA). In the first stage, we try to simulate a triangular fuzzy number by distributing it into some partition points. In the second stage, we try to find out the best solution of the defuzzified shortest path problem using a two-population scheme. The empirical results show that the proposed two-stage GA can obtain very good solutions within the given bound of each imprecise arc length than other fuzzy shortest path approach.