Research On Urban Delivery Routing Planning Based On Road Transit Time

As traffic jams in the city get worse,enterprises’ s delivery cost keeps high.At the same time,customers have more desire for improve delivery efficiency.Therefore,a scientific distribution plan is especially important for logistics enterprises.Due to traffic flow and traffic accidents,road traffic time is dynamic,and different departure times have different delivery time and delivery cost.Therefore,it is of great theoretical and practical value to deeply study the time-dependent vehicle routing problem(TDVRP).The article aims to build TDVRP models using road continuous transit time functions and solve the model to provide enterprises with a scientific solution that includes delivery routes,optimal departure times,taking into consideration cost and customer satisfaction.It mainly includes the following four aspects:The article analyzed and improved the road transit time function in the TDVRP model and established a continuous time function model.After comprehensively analyzing the acquisition schemes of the three kinds of time data,we used map software as the basic data acquisition method for this article.According to the rules of road traffic data,the Gauss mixture theory was used to construct the road network time function of the actual road network by Matlab.Combined with the actual data,.the results showed that the continuous function could more accurately reflect the relationship of departure time and travel time than the piecewise time function.The road transit time function was used to build single-objective,multi-objective TDVRP model.The former was used the minimization cost as the objective function to find the delivery route and the best departure time when the cost was the lowest.The latter was built a customer satisfaction function based on the client’s time window,established a model that minimizes delivery cost and maximize customer satisfaction as the objective functions,and looked for delivery routes and optimal departure times that took into account both cost and customer satisfaction.Aiming at the characteristics of models,we designed a two-stage algorithm to solve this function.In the first stage,taking the shortest path between the distribution center and the customer as the weight value,Dijkstra algorithm was used to find the shortest distance between distribution centers and customers.And we planned distribution route under the consideration of constraints.In the second stage,the genetic algorithm was used to calculate the optimal departure time and cost for each path.In the case of multi-objective solution,the customer satisfaction interval that satisfies the requirements was first obtained,and this interval was used as the upper and lower limit of the genetic algorithm’s independent variable,and then the optimal starting time of the lowest cost was calculated.The actual case analysis showed that the model was practical and universal.By analyzing road transit times,planning delivery routes,and choosing an optimal departure time for delivery,it reduced corporate delivery costs and improved customer satisfaction.