Optimization Of Order Picking And Storage Slotting In Automated Warehouse

With the development of modern science and technology and global economic integration, modern logistics has been an important part of modern economy. Modern logistics is the most economic and reasonable integrated service mode in the process of industrialization. It is the third source of profits after saving resource and improving labor productivity. Automated warehouse can greatly improve the efficiency of the overall logistics operation and plays a significant role in the logistics system. Good storage allocation can not only make full use of the limited space, but also reduce the time of accessing and choosing goods, which can improve the goods’ turnover and warehouse’ operation efficiency. Reasonable picking route can not only shorten the equipment’s walking path and picking time, but also reduce energy consumption in the picking process. What’s more, reasonable picking path can reduce costs, improve equipment’s utilization and reduce the idle and waste of resources. So it’s useful and significant to research the optimization of storage slotting and order picking in automated warehouse, which can improve the overall level of warehouse operation and improve the economic benefits of enterprises.Based on the existing research, this paper investigates the problems about the automated storage slotting optimization and the optimization scheduling of order picking. The main contents of this paper are as follows:① This paper expounds the general situation of the development of automated warehouse and analyses the research status at home and abroad of the optimization of storage slotting and order picking in automated warehouse. At the same time, this paper discusses the related calculation theory of automated warehouse which lays the foundation for follow-up study.② This paper establishes the simulation model of automated warehouse by Flexsim simulation software. Taking the operation of distribution center as an example, this paper analyses the equipment’s rationality of the system, so as to provide reference for the optimization research of storage slotting and order picking in automated warehouse.③ Unit shelf is determined as the research object and this paper establishes multi-objective optimization models with the aim to maximize the goods’ loading and unloading efficiency, the shelf’s stability and the correlation between one and another. In the process of research, aiming at the shortcomings of multiple objective particle swarm optimization, this paper improves the algorithm from three aspects including external archive update, the choice of learning samples and particle variation. Then the computational complexity of the improved multiple objective particle swarm optimization is analyzed. Finally, the test functions prove the effectiveness of the improved multi-objective particle swarm optimization algorithm.④ This paper analyses the process and principle of order picking, and clarifies the essence of the order picking path optimization problem is a special kind of TSP. Then this paper establishes the picking path optimization model with the goal that stacker has the shortest picking route. Aiming at the shortcomings of the basic particle swarm algorithm, this paper improves particle swarm optimization from three aspects including inertia weight, the parameters of acceleration factor and the global optimal position. The improved particle swarm optimization algorithm is proved superior through the test function.⑤ The improved optimization algorithm is used to solve the storage slotting optimization model and the picking path optimization model. The simulation results show the rationality of the model and the effectiveness of the improved algorithm.