Optimization of cold chain logistics distribution path for customer classification based on combination empowerment

Under the trend of big data, the long-term development of cold chain distribution companies, in addition to path planning, is increasingly inseparable from refined management of customers. Exploring customer value based on customer historical order records and rationally allocating limited distribution resources has become one of the development needs of various enterprises. This article establishes evaluation indicators through customer historical consumption orders, and uses the DBSCAN algorithm to implement customer classification after weighting. Different customer penalty costs are established according to different customer categories. At the same time, considering that customers will be dissatisfied with product quality after timeout, an overtime quality penalty cost is established. Based on the characteristics of the problem, a hybrid genetic particle swarm algorithm was designed to solve the problem, and compared with the distribution costs of unclassified customers, the effectiveness of the algorithm and model was verified.