Research on multi-AGV scheduling for intelligent storage based on improved genetic algorithm

Abstract

Intelligent storage has become an important part of various logistics industries, and task assignment of multi-mobile robots is an important part of intelligent storage. In this paper, the robot transport cost and no-load operation cost and task completion time cost and task assignment balance are used as optimization objectives. An improved genetic algorithm is proposed for the optimization of task assignment of multi-mobile robots. By establishing a mathematical model; adaptively adjusting the crossover probability and the fitness function of the improved genetic algorithm are used to improve the convergence speed and convergence of the population. Example simulations show that the improved genetic algorithm converges faster and has a better assignment.