| In recent years,the national policy and system reform for medicine are gradually increasing with the gradual improvement of national medical care and quickening the population aging,and then pharmaceutical circulation industry is directly affected by pharmaceutical policies.The structure of orders has changed obviously in pharmaceutical logistics distribution centers,which tends to be fragmented gradually.And this situation has contributed to the sharp increase of the picking orders.Due to its inefficient operation disadvantages,the traditional PTG(people-to-goods)picking mode is bound to be replaced by the GTP(goods-to-people)picking mode with the development of automatic and intelligent equipment.Therefore,the optimization of the goods-to-people picking mode is becoming one of the popular research issues in the application of the pharmaceutical circulation field.From the perspective of order processing,this paper studies the optimization of system operation in the goods-to-people picking mode,and mainly completes the following work:1.This paper analyzes the impact of the superposition effect of medical policies,which the structure of orders has changed in the operation of pharmaceutical logistics distribution system,and then analyzes the problems that need to be solved synthetically.The picking system is divided into upstream subsystem and downstream subsystem.Order processing strategies are proposed to optimize the two subsystems on order batching and order allocation.2.Under the background of pharmaceutical circulation industry,aiming at the problem that the operation efficiency of the elevator in the upstream multi-shuttle storage system is limited,and that it is difficult for bin operations to meet the increasing demand for input and output of system,a rough batch dimensionality reduction strategy based on EIQ analysis is proposed to realize the rough batch of the massive order data.Based on the data preprocessing combined with the dimensionality reduction idea,the orders are optimized in clusters and batches by designing an improved K-means clustering algorithm.And suggestions are proposed for improving the system's actual picking mode combined with the order characteristics of EIQ analysis.3.Under the background of pharmaceutical circulation industry,this paper introduces the concept of precise dynamic planning,aiming at the problem of the low efficiency on the picking stations due to the unreasonable distribution of bins and orders.An iterative optimization algorithm is used to solve the orders and bins planning problems in a single picking station based on Beam Search algorithm.Based on this,this paper designs the decoupling algorithms for order allocation of multiple picking stations,which the dynamic planning process of the bins is put forward in the bin selecting process at each stage.4.Taking a pharmaceutical e-commerce company as a research example,the model in this paper is simulated and verified.For the order batch problem,the IK proportion and the correlation coefficient threshold range are determined by training the model.Combined with Python simulation,the optimization effect of the algorithm proposed verify compared to existing operation mode in this paper.For the order allocation problem in the context of pharmaceutical batch constraints,Python simulation verified the significance of the optimization effect which of the order iterative optimization allocation algorithm on the single picking station and order allocation decoupling algorithm on the multiple picking stations.Furthermore,the AutoMod simulation is used to build the model according to the actual scene and verify the output of the decoupling algorithm which is about the running track of each bin in the actual operation of the system.The result shows that the system efficiency is improved compared to the existing model.It can be used for reference to the order processing strategies in the actual operation of the enterprise related picking system. |
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