The delivery of the front position mode can effectively solve the customer's requirements for the timeliness of the order, which is an emerging distribution model. However, its management model, order distribution, path division, etc. lack scientific guidance, which will lead to an increase in end distribution costs. This article studies the dynamic division of the distribution area of the preparatory distribution staff. This problem is mainly divided into two stages for discussion. According to the total distribution capacity of the front warehouse, the demand is changed, and the use of total distribution capabilities can maximize the use of total distribution capabilities. In the first stage, the initial distribution area is divided by the K-means cluster to determine the optimal clustering number of the initial area. In the second stage, discuss the relationship between the total distribution capacity and the total demand in the initial region, and then adjust the initial area division by the path optimization. When the total distribution capacity meets the total demand, but the local distribution capacity is less than the local demand, the region is dynamically adjusted by path optimization. about. When the total distribution capacity does not meet the total demand, add third -party distribution to build a dynamic adjustment model containing third -party distribution. The adjusted third -party dynamic model is reduced by about 2%compared to the total distribution cost of non -adjusted third -party distribution. Both dynamic adjustment models are solved with ant colony algorithms. Dynamic adjustment models can reduce total distribution costs.
Front-warehouse, End-of-line distribution, Dynamic paths, K-means cluster
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