Dynamic Scheduling Model of Rail-Guided Vehicle (RGV) Based on Genetic Algorithms in the Context of Mobile Computing

2. Main Focus Of The Article

Many researches now studying the rail guided vehicle (RGV) mainly focus on the loading/unloading under certain distributions of the equipment. And most of them use artificial intelligence and machine learning to find the most efficient solution. But these methods require a great deal of data and cost much time. Meanwhile, what we are trying to solve is to figure out the optimal distributions of the equipment to save as much time as possible. So, from the perspective of the problem, what we are interested is a little bit more difficult and complicated since there are more uncertainties. Moreover, from the perspective of the modeling, we don’t have so much data that we can rely on to do the experiment and we need the models to give better solutions as soon as possible. On the whole, there are not so many researches we can refer to and we construct this dynamic model in a creative way.