Research On Optimization Of Cold Chain Logistics Network Of Fresh Agricultural Products In Large Chain Supermarket

With the development of the logistics industry and the improvement of the level of the per capita national economy,the demand for fresh agricultural products is increasing.At the same time,the demand for the cold chain logistics service is also higher.The problems such as imperfect network layout,inappropriate transportation mode and unreasonable distribution route restrict the development of cold chain logistics in the existing agricultural products cold chain logistics.In view of the problems existing in the cold chain logistics network,this paper chooses the optimization problem of the logistics network of fresh agricultural products of large chain supermarket as the research direction.The conclusion has positive significance on solving the actual problems existing in the current cold chain logistics network and promoting the development of cold chain logistics of fresh agricultural products.On the basis of summarizing the related problems at home and abroad,this paper describes the cold chain logistics network.According to the characteristics of the cold chain logistics of fresh agricultural products,the paper has fully considered the important influence of the cost of damage and energy consumption on the optimization of the cold chain logistics network,and established a cold chain logistics network optimization model for fresh agricultural products in large supermarket chains based on bi-level programming.The upper level model is the total cost optimization model of the cold chain logistics network,which solves the problem of supermarket layout coverage location and vehicle routing optimization.The lower level model is a customer satisfaction optimization model to solve the optimal allocation problem of supermarket demand attraction.Finally,the improved genetic algorithm is used to solve the bi-level programming model,and a case study of the large chain supermarket Y is carried out to verify the correctness and scientificalness of the model and algorithm.