Research On Order Batch Picking And Distribution Optimization Under The Mode Of Community Group Purchasing

With the development of fresh e-commerce,community group buying has been increasing year by year in the scale of fresh e-commerce transactions due to its advantages in warehouse allocation and price.The coexistence and development of multiple warehouse allocation models in community group buying have led to an increasing number of customer demand categories,increasing link delivery costs,and increasingly fierce competition among enterprises.This poses new challenges to the supply chain and logistics delivery of community group buying.Improving logistics delivery capabilities and reducing the costs of each delivery link are of great significance for the development of community group buying enterprises.This article focuses on the delivery network of community group buying goods.Firstly,a multi-objective batch picking optimization model considering the shortest picking distance and minimum number of order batches considering the attributes of goods is established for the batch and picking process of order goods in the distribution center(grid warehouse).The objective transformation method is used to target the minimum order batch number into a batch number constraint,and a Firefly algorithm is designed to improve the firefly location update strategy.The efficiency of the algorithm is verified through simulation experiments.By comparing the partitioned picking model considering goods attributes established in this article with the non partitioned picking model,the picking batch of this model is reduced by 9 batches,the labor cost is saved by 1/4,the average picking distance is saved by 48.5%,and the average picking time is saved by 22.9%.Secondly,based on the research of order batch model,the joint optimization problem of order batch picking and delivery under community group buying mode is studied.Based on the Weibull distribution of the loss and deterioration cost of fresh products,the labor cost of order picking,the transportation cost,the Fixed cost of vehicles,and the time window range and loading capacity constraints of each cluster,a two-level optimization model of batch picking and distribution with the minimum total cost is constructed.Design a genetic firefly hybrid algorithm with local search function to solve the model.The main body is to solve the vehicle routing problem with fresh consumption and time window in the outer layer by genetic algorithm,and the Firefly algorithm solves the order batch routing and cost under the corresponding path,which is fed back to the fitness function of the outer layer algorithm,and at the same time,solves the optimal distribution path and the employee picking batch of the corresponding vehicle.Finally,combined with the data of community group buying nodes in Huangpi District of Wuhan City,MATLAB was used to solve the case optimization scheme,and model comparison verification and sensitivity analysis were carried out.The experimental results show that the genetic Firefly algorithm designed in this paper can effectively solve the order batch picking and distribution double-layer model,and the algorithm has strong search ability and high stability.Compared with the two-stage algorithm solving model,the joint optimization model proposed in this article reduces the number of picking batches by 5,saves labor costs by 1/5,and reduces total costs by 488.3 yuan,saving 9%.As the scale of case nodes increases,the cost saving effect will be more significant.The order batching and distribution optimization problem of the "grid warehouse group point" link under the community group buying model studied in this article is an extension of the traditional order picking and fresh food path optimization problem,enriching the solutions to the joint optimization problem of picking and distribution,and has certain reference significance for enterprises and other types of online fresh food sorting and distribution.