Research On Robot Scheduling Model Of "Rack To Picker" Picking System

With the development of e-commerce,people's lifestyles have gradually changed,and online shopping has become an indispensable part of people's lives.Under the background of uncertain consumer demand,the supply of goods has gradually developed to multiple varieties,small batches,and multiple frequencies,leading to frequent updates of warehousing data,diverse information structure changes,and complexity of picking tasks,so the logistics industry is facing huge challenges.As the center of logistics operation,the warehousing system is a key way to improve the efficiency of logistics operations.In order to improve market responsiveness and customer satisfaction,the warehousing system has gradually shifted from traditional manual operations to automation and intelligence,by using the "rack to picker"picking system with multi-robots to work in parallel instead of the traditional manual handling system,which not only improves the operational efficiency of logistics,but also greatly reduces labor costs.In the "rack to picker" picking system,how to dispatch each robot scientifically and rationally to efficiently complete the tasks in the system is of great significance for improving the efficiency of logistics operations and reducing logistics costs.Aiming at the robot scheduling problem of the "rack to picker" picking system,this article first analyzes the operation process of the "rack to picker" picking system.A random scheduling robot strategy is proposed in the case of tasks being issued in batches.The experimental results show that the random scheduling robot strategy has higher picking efficiency.Secondly,by analyzing the cost composition of robot scheduling in the "rack to picker" picking system,a random scheduling robot strategy is adopted.The shortest total time for the pickers to complete all tasks is the objective function to optimize the order completion time,and the robot scheduling is considered as a decision variable.Consider factors such as the waiting time,picking time,and distribution time of pickers at the picking workstation,and establish a robot scheduling model.The genetic algorithm is used to solve the problem,and the simulation of an example verifies that the robot scheduling model proposed in this paper can quickly complete the order by optimizing the total time for the picker to complete all tasks.Finally,by analyzing that the robot's waiting time at the picking station during the completion of the task is too long' the path will be congested,which will affect the progress of other robots to complete the task.Therefore,it is established to consider both the robot's waiting time and the total time for the picker to complete all tasks.The multi-objective optimization model of robot scheduling uses the constraint method to process the multi-objective optimization model,and the genetic algorithm is used to solve the model on MATLAB software.The final result shows that the obtained robot scheduling result improves the robot waiting time while reducing the waiting time.