Research On Task Allocation Of Multi Picking Workstations Of GTP Robot System In Medicine Distribution Center

At present,the pharmaceutical circulation industry is affected by the relevant policies of the national medical reform,and faces the dilemma how the pharmaceutical logistics center completes the picking operation of a large number of piece picking orders within the limited time from the medical terminal orders to deliver the goods,that is,how to improve the picking efficiency.The goods-to-person order picking system based on the Automatic Mobile Robot system has been proved to be one of the important means to solve the dilemma of the piece picking through practical application.This paper mainly studies the order problem in the goods-to-person order picking system based on the Automatic Mobile Robot system.The main research contents are as follows:In this paper,for the order picking problem of the “goods-to-person” picking system with the limited of transportation channels in goods-to-people order picking system based on Kiva system,the order batching algorithm under wave processing orders is designed:batching of wave processing orders,aiming at minimizing the total number of inbound and outbound containers.An order batch mathematical model considering the degree of clustering of batch results was established,and a subdivision batch algorithm based on k-means algorithm was designed to realize order batching with controllable clustering degree;Secondly,Multiple picking workstations of "goods to people" picking system are often be used in pharmaceutical logistics center.In order to avoid the order coupling problem brought by the traditional order assignment strategy with the restriction of batch management of medicine,the order assignment problem of the goods-to-person order picking system is analyzed,and the mathematical model of the order assignment problem is established,and in order to minimize the order picking completion time,the optimal container arrival order is obtained.In order to solve the problem that the standard genetic algorithm is easy to fall into the local optimal solution when solving the large-scale order quantity,an adaptive genetic algorithm based on neighborhood search is proposed.Compared with the standard genetic algorithm,the optimization is carried out in two aspects: 1.Adopt adaptive crossover and mutation probability to avoid destroying the excellent offspring;2.Add the neighborhood search after the mutation operation to improve the local search ability of algorithm in order to avoid premature convergence of the algorithm.Finally,in order to verify the validity and reliability of the model and algorithm,MATLAB is used as the simulation platform to compare the performance of the algorithm.The simulation results show that the adaptive neighborhood search genetic algorithm is superior to the standard genetic algorithm in the aspect of the average deviation distance and the standard deviation of the solution.In terms of algorithm running time,the standard genetic algorithm is better than the adaptive neighborhood search genetic algorithm.This paper mainly studies the order assignment problem of the goods-to-people order picking system based on Kiva system with multiple picking workstations and some experiments were carried out in Sinopharm group project.The related models and algorithms can provide some reference for the practical application of the pharmaceutical circulation industry,and specify directions for subsequent related research.