A Scheduling Optimization Method for Circular RGV Systems Based on Improved Partheno-Genetic Algorithm

The scheduling problem of Rail Guided Vehicle (RGV) has a significant impact on warehouse efficiency, which in turn affects the efficiency of production logistics and modern production as a whole. In this research, an improved Partheno-genetic Algorithm is proposed to optimize the job scheduling of a circular RGV system. It omits the traditional crossover operation and instead utilizes a single-point interchange operator as a replacement for the conventional mutation operator. A real-world case study shows that this approach outputs less transportation time with high computational efficiency and global optimization capability. The computational results are further validated by a simulation platform developed using FlexSim software.