Using Distance Landscape Strategy for RGV Dynamic Scheduling Problem

Rail guide vehicle (RGV) dynamic scheduling problems have attracted increasing attention in recent years, which determines a great impact on the working efficiency of the entire scheduling system. However, the relative intelligent optimization study of RGV dynamic scheduling problems are insufficient scheduling of different working components in the previous works, it is easy to appear idle waiting, resulting in reduced operating efficiency during operation. Analysis of the fitness landscape is essential to understand the behavior of evolutionary algorithms for solving dynamic optimization problems in the evolutionary dynamics of biological evolution. With the continuous advancement of evolutionary algorithm optimization, the fitness landscape can present more abundant feature information around the fitness value, including autocorrelation, fitness distance correlation, landscape walks, local optima, and landscape roughness. This paper proposes a new distance landscape strategy for the RGV dynamic scheduling problems. The combination of the fitness landscape and dynamic search strategy are established according to the operating principle of the RGV system. In order to solve the RGV dynamic scheduling problem more effectively, experiments are conducted based on the type of computer numerical controller (CNC) with one procedure programming model in solving the RGV dynamic scheduling problems. The experiment results reveal that this new distance landscape strategy can provide promising results and solve the considered RGV dynamic scheduling problems effectively.