Research On Vehicle Routing Problem With Backhauls In B2C E-commerce Environment

In the past few years,especially in dealing with the recycling of environmental protection,the Vehicle Routing Problem with Backhauls(VRPB)has attracted a lot of attention.The emergence of backhauls not only reduces the cost of logistics and transportation,but also makes full use of social resources.In practical problems,in order to improve the quality of service,enterprises will meet the specific service time required by customers.As the service time is reduced,the efficiency of logistics and transportation will be improved,and thus the costs will be further saved.Therefore,this paper studies the Vehicle Routing Problem with Backhaul(VRPB)and Vehicle Routing Problem with Backhaul and with Time Windows(VRPBTW).The main contents are summarized as follows:First,build the model.In the traditional logistics distribution mode,the concept of pre-sales mode is introduced.The pre-sale mode is to quickly gather a single scattered customer demand order in a short period of time,so that orders can be more concentrated.When designing the distribution plan,Avoiding more duplication of labor and achieving resource conservation is not only conducive to sustainable development,but also reduces logistics costs.On this basis,a single objective function is established to minimize the sum of vehicle driving cost and vehicle fixed cost.A single objective model that minimizes the sum of total vehicle driving cost and total time penalty cost,taking into account time cost and customer service satisfaction.This paper establishes a model with soft time window constraints.Second,algorithm design.In this paper,the k-means clustering algorithm and the Tabu Search Algorithm(TS)hybrid heuristic algorithm are used to solve the problems.The principle of the tabu search algorithm is to improve the global optimal solution and the current optimal solution through tabu and special criteria from an initial feasible solution,so as to achieve the purpose of finding a satisfactory feasible solution.The tabu search algorithm relies on initial solution.A high-quality initial solution can find a final high-quality distribution plan in the solution space,and improves the convergence speed of the tabu search algorithm.This paper uses the k-means algorithm to obtain a valid initial solution.Compared with the solution obtained by Genetic Algorithm(GA)and tabu search algorithm,it proves that the tabu search algorithm is simple,easy to implement and has high efficiency.Therefore,the algorithm is selected to solve the model.Third,simulation experiment.According to the established mathematical model and the designed algorithm,relevant data cases are summarized.Through the Python software programming,the high-quality solution is given,and the better transportation route scheme is given.And the experimental results show that the algorithm runs better.On this basis,analyze the setting of different parameters,such as the number of iterations,the cluster center,the length of the tabu table,etc.,select the appropriate parameter values to verify the algorithm and the model.The results show that the feasibility and efficiency of the algorithm can reduce more transportation costs and time costs.