Automated Warehouse Library Job Scheduling Optimization

As the key part of modern logistic system, Automated Storage And Retrieval System(AS/RS) is widely applied to all walks of life. Unloading task scheduling in the warehouse is an important part of AS/RS. Whether the unloading task is quickly and accurately carried out or not determines the system performance to a large extent, which directly affects the interests of the businesses and customers. Therefore, the study on AS/RS is of practical significance. This paper focuses on the following problems of scheduling methods of unit unloading task and selected unloading task in the automated warehouse.First of all, it elaborates on the physical structure and logical constitute of the automated warehouse, analyzes the stacker as well as the tasks it carries out, and discusses in detail the mode and workflow of the unloading task. Next, on the basis of understanding the principles of the unloading task scheduling and cargo space management, it analyzes the scheduling strategies of the unloading task, designs a scheduling algorithm of the unit-unloading task, which is based on the three-dimensional priority table of the three characteristic parameters, and simulates the actual situation of unloading task scheduling. Experimental data show that the Hit Value Ratio and Differentiated Guarantee Ratio are both higher than that of the classic unloading task scheduling algorithms and Two-dimensional priority scheduling algorithm, which shows certain improvement on the efficiency of unloading operations. Finally, it abstracts the procedure of selected unloading task to a traveling salesman problem (TSP), according to the mathematical model for the selected task, uses the improved genetic algorithm to solve the problem. Experimental data show that compared with the traditional genetic algorithm, the improved genetic algorithm delays the occurrence of prematurity, expands the diversity of the species, and increases the probability of the global optimal solution. Therefore, there is optimization of the stacker's working path and improvement on the efficiency of the selected unloading task.