A Real-Time Delivery Path Planning Method with Customer Classification Priority
In order to enable real-time delivery enterprises to improve delivery punctuality at lower costs,meanwhile maintain and develop high-value customers,firstly,the RFM model was improved based on the characteristics of real-time delivery customers,and customer clustering based on existing data was realized using the DBSCAN algorithm.Using the GBDT algorithm,a customer grading prediction model based on the clustering results was constructed to predict the grading of customers.On this basis,with the fixed and variable costs of instant delivery,as well as the types and quantities of customer timeout points as optimization objectives,a real-time delivery path optimization model based on customer classification was constructed,and a genetic algorithm was designed to solve the model.Finally,a case study was conducted on a one-stop cold chain real-time delivery enterprise in Shenyang.The results showed that compared to the original delivery plan of the enterprise,the plan planned using real-time delivery path planning method with the customer classification priority only increased the total delivery cost by 4.8%.Meanwhile the number of high-value and potential high-value customer timeout points de-creased from 6 to 2,and the timeout points were all edge customers.At the same time,the total de-livery time decreased by 7.3%,verifying the effectiveness of the planning method.By adopting this delivery path planning method,although the delivery cost of the enterprise may slightly increase,the improvement of delivery punctuality can better maintain high-value customers,while developing po-tential high-value customers and transforming them into high-value customers,thereby maintaining or improving long-term profits.
万方数据
维普
