Optimization Of Order Batching And Multi-Robot Task Allocation In Parts-to-Picker Picking System

The rapid development of the e-commerce industry makes the warehouse face more and more orders,which requires higher and higher order picking efficiency and accuracy,and promotes the development of parts-to-picker picking systems based on handling robots.In the parts-to-picker picking systems,according to the order information,the robots will move the corresponding mobile shelves to the picking station to cooperate with the manual to complete the order picking.In order to improve the operating efficiency of the system and quickly respond to order needs of customers,it is necessary to adopt an appropriate order picking process.In the entire order picking operation,order batch picking and task allocation of handling robots are the key links that affect the operation efficiency.Therefore,this paper proposes an order picking process: first order batching,and then allocating the batches to picking stations,and finally allocating the order batches belonging to picking stations to the shelves that need to be transported to the handling robot.The research focus of this paper is on order batching and multi-robot task allocation,so as to optimize the order picking operation in the system and improve the order picking efficiency.Firstly,this paper analyses the pick-to-people picking system,expounds the composition,overall layout,operation mode and specific operating process of the pick-to-people picking system,and uses the grid method to establish a picking operation environment model.Then,this paper researches the order batching to provide a basis for the research of multi-robot task allocation.An order batching model with the minimum number of shelf movements as the target is established,and two algorithms solution similarity rules and variable neighborhood search is proposed.Considering the balance of the operations of each picking station,the order batches are evenly distributed to each picking station.Based on the results of order batching,research on multi-robot task allocation is conducted.After completing order batching,the shelves required for each batch of orders are determined,and shelf handling is regarded as a task that the robot needs to perform.In order to describe the relationship between robot handling tasks and the cost consumed by the robot to complete the handling task itself,a multi-robot task allocation model is established by introducing task correlation functions and task own cost functions.Using the auction algorithm,minimizing the total moving distance of handling robots is the optimization goal,design bidding and winning strategies,and finally find the optimal task allocation plan.Finally,in the same picking system environment,for the same order information,use MATLAB to conduct comparative experiments.Compared with the direct order picking strategy,that is,all orders are randomly assigned to handling robots,the order batch picking strategy optimizes the multi-robot task allocation results,which can shorten the movement distance of robots by about 50% and reduce the task of robots by about 50%.In addition,sensitivity analysis is performed on the important parameters of the parts-to-picker picking system including the number of handling robots,the number of orders,and the number of storage shelves.There are 18 figures,16 tables and 104 references in this paper.