Using a Fuzzy Genetic Algorithm for Solving Transportation Logistics Problems

Recently, Science defined transportation as the most potent component of logistics. In addition, it has an interdependent relationship with business Also, AI is intervened in logistics to solve issues. used for optimizing and obtaining possible solutions critical complex problems. This paper aims optimize costs profit get satisfaction individuals organizations using techniques. A proposed methodology consisted two phases. The first phase discusses data collection, second involves applying FGA Artificial Intelligence Transportation Logistics model was determine boundary each Product, a Fuzzy Genetic Algorithm done According that, outcomes were detected by cost detecting parent's child's chromosomes, took number iterations =2000. between 100 loops, best 5 loops 1.53 Millie seconds per loop Using GA. Similarly, GA minimum total Product also determining parents child which Number iterations= 2000, among five 1.40 ms loop. Moreover, predicted triangular fuzzy logic shows that considered (20 million 23.9 million), while moderate (24 maximum more than (24.1 million).