Goods-to-Person Order Picking Research Based On Swarm Intelligence Optimization Algorithms

With the rapid development of the Internet,online shopping has enriched people’s life,promoted the development of the e-commerce industry,and put forward higher requirements for warehousing order selection.The logistics industry pursues efficient and fast order selection,while the traditional person-to-goods order picking mode is generally low efficiency and high cost.Goods-to-person order picking mode has the advantages of high efficiency and intelligence,so it has been rapidly developed.After the picking system issues action instructions according to the order information,the robot moves the shelves to the picking table,and the picker selects the goods according to the operation process to complete the order picking operation.Batch picking,task assignment and path planning are the key links that affect the efficiency of order picking.In order to effectively improve the accuracy and efficiency of order picking,and timely meet customers order needs,this thesis studies the problem of goods-to-person order picking.The specific research contents are as follows:(1)This thesis analyzes the goods-to-person order picking system,introduces the basic layout and operation process of the system,and uses the raster method combined with the two-dimensional rectangular coordinate system method to build the order picking environment model.An order batching model with the goal of minimizing the total number of moving shelves is established.The order similarity calculation rule is proposed,and the order batching result is obtained by using Firefly Algorithm optimization.(2)After the order is completed in batches,determine the shelf number that needs to be handled in each batch.A mathematical model aiming at minimizing the total distance of the robot is established.The cosine convergence factor and particle swarm optimization algorithm are introduced to improve the Grey Wolf Optimization Algorithm.The improved algorithm is used to solve the model and assign shelf-moving tasks to robots.After the task assignment of the shelf is completed,the speed variable is introduced,and the priority is set according to the length of the remaining time of the robot to complete the task.The mathematical model aiming at the shortest total handling time of the robot is established.The vector angle value is introduced to improve the evaluation function of A*algorithm,and the improved algorithm is used to solve the model,plan the robot travel path,and complete the goods-to-person order selection.(3)Complete the comparative experiment under the premise that the environment of order picking system remains unchanged.Firefly Algorithm is used to optimize the order batch,and the optimization effect is significantly improved compared with the total number of shelf handling required by the original order selection.The Improved Grey Wolf Optimization Algorithm for solving the shelf task assignment problem is compared with the original algorithm,mixed Grey Wolf Optimization Algorithm(PSO_GWO)and Grey Wolf Optimization Algorithm based on Cuckoo Search Algorithm(IGWO).The experimental results verify that the proposed method needed the shortest total distance for the robot to solve the problem.The A*algorithm for planning and picking the path problem is compared with the original algorithm and the dynamic weight factor improved A*algorithm.The experimental results verify the rationality and feasibility of the proposed method,and effectively improve the efficiency of goods-to-person order picking.