Research On The Method For Vehicle Routing Optimization In Urban Logistics Distribution

Logistics is the artery and basic industry of national economic development,and its development degree is one of the important symbols to measure the comprehensive strength of a country and region.As a core problem in logistics distribution,the optimal solution of Vehicle Routing Problem(VRP)is very important to the whole logistics transportation speed,cost and benefit.Based on this problem,this paper studies the related optimization algorithm theory of VRP,and applies the improved algorithm to VRP.The main achievements and innovations of this paper are as follows:1)A new branch-and-cut method is designed for integer programming.By studying the exact algorithm for VRP and the cutting plane method of integer programming,according to the canonical expression of the objective function,this paper constructs a kind of inequality with the optimal value of the objective function as the parameter,and proves that the inequality is effective.Numerical results show the effectiveness of the proposed algorithm.Combining this method with the branch and bound method based on pseudo contribution branch strategy,a new branch-and-cut method for integer linear programming is designed.2)An improved tabu search algorithm based on I&D search strategy is proposed for CVRP.Considering that the tabu search algorithm is easy to fall into the local optimum in the search process,I&D search strategy is introduced into the tabu search algorithm to make the algorithm jump from one local optimum to another improved local optimum quickly and improve the search efficiency of the algorithm.Experiments show that the improved algorithm has higher accuracy and speed than the standard tabu search algorithm.3)An improved genetic algorithm based on Metropolis criterion is proposed for VRPTW.In order to overcome the shortcomings of the local search ability of genetic algorithm,this paper proposes an improved genetic algorithm based on Metropolis criterion,which combines annealing operation with genetic operation to retain the optimal chromosome in each generation and accept the inferior chromosome with a certain probability.The probability that the search can jump out of the local optimal solution is increased to realize the global optimization.Some examples in Solomon standard test library are used for experimental data,and the results show that the improved genetic algorithm can obtain better solutions for different customer sizes.