Research On Vehicle Routing Problem With Soft Time Windows Considering The Priority Of Customers

With the advancement of technology and the increasingly fierce market competition,reduce logistics costs as "the third profit source" has become the most important competitionarea of company which attracts great attention. Logistics distribution is a key link of logisticssystem and accounted for a high proportion of logistics costs. Each company has been tryingto cut the distribution cost which is also a hot of modern logistics research field. Distributionwhich involves an optimization study of Vehicle Routing Problem (VRP) aims to completethe delivery meeting customers’ requirements with an optimal cost. A vehicle routing problemof which the customers have limited time window is VRPTW. The traditional VRPTW studiesthe optimal vehicle path with the consideration of transport costs and penalties arising fromthe violations which can acquire the minimum of transport costs and time penalty cost in thecondition of meet customers’ demand. However, the traditional VRPTW neglect the influenceof customer satisfaction on enterprise. Some customers especially with great significance willreduce the value brought by them if the service get is unsatisfactory, which may cause acertain amount of loss.The VRP theory and customer importance theory are introduced and the potential valueof the customer is analyzed according to the theory of important customer value, then measurethe importance of customers with its potential value. The potential value of customer willachieve the maximum if it is satisfied, namely, it can bring greater profits to the enterprise.Indicate the customer’s satisfaction by the total potential value of customers, and then aVRPSTW model to minimize the total cost consisting of transportation costs and time penaltycost and to maximize the total potential value of customers is studied based on the traditionalvehicle routing model. A small-scale data is used to test the validity of the optimizationVRPSTW model established considering customer importance, and a comparison withVRPSTW model without consideration of customer importance is made to highlight theusefulness of the model established in this paper. An improved genetic algorithm is designedto solve the model and numerical experiment using Solomon standard data is implemented ofwhich the experimental results demonstrates further the accuracy of the model in this paperand the effectiveness of the algorithm designed.