Optimal Model of Chicken Distribution Vehicle Scheduling Based on Order Clustering

Abstract

In order to solve the problems that orders are widely distributed, scheduling of distribution vehicle needs a lot of manpower,and high cost of chicken distribution in large-scale poultry enterprise, in this research, combined with the idea of solving vehicle routing optimization problem, a chicken distribution vehicle scheduling optimization model based on order location clustering was proposed. By introducing the K-means clustering algorithm, a distribution unit division method based on order location was implemented, an automated order location clustering process based on the elbow rule and contour coefficient method to realize the autonomous division of order distribution units was designed. On the basis of the divided groups of orders, the optimal delivery cost was taken as the objective function to establish a chicken delivery vehicle scheduling optimization model, and the model was solved with an improved genetic algorithm.The actual order data of a poultry company in Beijing was used to compare the results of the overall scheduling optimization in the case of orders without clustering and the scheduling optimization in the case of with clustering grouping. The results showed that the model in the case of orders with clustering could reduce the average daily mileage of delivery vehicles by 69% compared with orders without clustering, it could be seen that the optimization of order grouping with clustering algorithm was more suitable for vehicle scheduling scenarios with a large actual order position span and a large number of orders. Based on the above research, a vehicle scheduling optimization service system was developed, functions such as automatic order clustering, delivery vehicle scheduling optimization were realized, and model service application programming interface was customized.The practical application results of the model showed that, the average total mileage per day decreased by 5.04% compared with manual routing, the manual routing time took 20 to 30 minutes per day, and the average time for the model to complete the routing was 14.49 s. The goal of providing intelligent delivery vehicle scheduling optimization services for poultry industry enterprises has been achieved, which could effectively improve the operation efficiency and reduce the distribution cost of the poultry enterprise.

Keywords

• chicken delivery
• vehicle scheduling optimization model
• k-means clustering algorithm
• genetic algorithm
• smart poultry
• api