Research On Order Distribution And Path Optimization Of Chain Supermarkets

With the rapid development of network technology,the combination of the Internet and the traditional industry has become a trend,which has spawned a new business model,namely the O2 O business model.As a terminal place connecting consumers and many producers,chain supermarkets actively embrace Internet technology in the process of seeking transformation and upgrading,and adopt a new O2 O business model to deliver goods purchased online by customers.On the one hand,due to the timeliness,immediacy and stage characteristics of the terminal distribution service of the supermarket chain,the management difficulty of the supermarket operators has been improved.On the other hand,most of the distribution personnel in the chain supermarkets belong to the idle resources of the society,and they have not undergone much professional training.Therefore,at present,there are mainly problems of unmanned orders and overtime delivery in the terminal distribution process of chain supermarkets,and it is particularly important to improve and optimize them.This paper takes the distribution order distribution and path optimization of chain supermarkets as the research object,in order to improve the terminal distribution efficiency and enhance customer satisfaction.The main research work includes:1.The status quo of terminal supermarket distribution is analyzed.By sorting out the crowdsourcing distribution process and related data of chain supermarkets,the key problems that need to be solved in the terminal distribution process of chain supermarkets are summarized,including effective merger supermarket delivery orders and reasonable optimization of distribution.route.2.For the terminal supermarket distribution range is within 3 km,the distribution order allocation problem is mainly solved from the cluster analysis of the order and the preferred aspects of the crowdsourcing rider.Firstly,the hybrid clustering algorithm is used to cluster the distribution orders according to the geographic coordinates of each consumer,so that the consumers in the same category are close to each other;Secondly,according to the relevant historical data of the crowdsourcing rider,the gray correlation degree method and the analytic hierarchy process are used to comprehensively evaluate the quality of the distribution service of the crowdsourcing rider,and the crowdsourcing rider with good service quality is selected to assign the collection order.3.A mathematical model of distribution path optimization with hard time windows is constructed for how to reasonably arrange the distribution route between multiple orders.Considering the satisfaction of the customer's delivery time window and the load limit of the vehicle,the total distribution cost is minimized as the objective function,and the constraints are established based on the specific situation of the terminal supermarket distribution.Finally,the mathematical model is solved by using the saving algorithm to obtain multiple delivery routes.4.Taking W chain supermarket as an example,the proposed order allocation model,distribution path optimization model and related solving algorithm are verified.The results show that the hybrid clustering algorithm has the advantage of high efficiency in the process of order clustering analysis,and does not need to artificially set the number of clusters.Through reasonable distribution route optimization,the total distribution driving distance can be greatly reduced,to a certain extent Reduce the total distribution cost;use the gray correlation method and the analytic hierarchy process to comprehensively evaluate the quality of the distribution service of crowd-sourced riders,and pick out the rider with good service quality to complete the delivery task.