Automated warehouse path optimization based on immunity discrete particle swarm optimization

Automated warehouse has been widely used in various industries, and how to further improve the scheduling efficiency of it is one of the key issues. In this paper, the storage and retrieval path scheduling of automated warehouse is considered as the research object. Firstly, the mathematical model of scheduling for the storage and retrieval path is established, which takes the shortest path as the optimization goal. Then an immune selection combines with discrete particle swarm optimization (IDPSO) is proposed to optimize the path, to avoid falling into local optimum prematurely, and to find the optimal solution easier. Experimental simulation results show that the model and algorithm are practical, and can improve the storage and retrieval operation effectively.