Research On Optimization Of Distribution Path Based On Progressive Volatility Factor Ant Colony Algorithm

With the rapid development of China's economy,people's consumption concepts and shopping methods have changed significantly.A large number of people have changed from the original offline shopping to the Internet online shopping,which has caused the phenomenon of online shopping based on e-commerce to become more and more popular among people.However,Internet shopping has driven the development of the logistics industry,and put forward a series of new requirements for the logistics industry.How to arrange transportation vehicles rationally and reduce transportation costs for logistics companies has become an urgent problem to be solved.The logistics distribution vehicle routing optimization problem is a variant of the classic vehicle routing problem and is also an NP problem.Therefore,the establishment of the corresponding mathematical model and the appropriate solution algorithm is the key to study this problem.The research and application of this problem can effectively reduce the transportation costs of the logistics industry,and can also reduce the carbon dioxide emissions of distribution vehicles,which has a positive impact on the economy and the environment.This this thesis mainly studies the modeling and solution of logistics vehicle routing optimization problems with time windows,and proposes a progressive volatile factor ant colony algorithm.The specific research work of this thesis mainly includes the following:Load the geographic location coordinates of each logistics site through Matlab software,build a logistics virtual simulation platform,and get the basic information of the logistics site.The vehicle distribution distance and customer satisfaction are the objective functions,and the parallel selection method is the multi-objective optimization method to solve the distribution path.In the set of distribution routes obtained,the optimal route is selected to complete the corresponding logistics distribution work.An ant colony algorithm for progressive volatilization factors is proposed.The traditional ant colony algorithm is improved correspondingly in the increment of volatilization factor,heuristic factor and pheromone.The improved ant colony algorithm sets the progressive volatilization factor according to the search stage,and considers the relationship between the starting point,the end point and each node in the heuristic factor.Compared with the traditional ant colony algorithm,the improved ant colony algorithm has a faster convergence speed,and it is easier to find the global optimal solution in a shorter time.Finally,the improved ant colony algorithm proposed in this thesis is simulated and verified on the virtual logistics simulation platform.The experimental results show that when the number of customer points is 20,compared with the traditional ant colony algorithm,the improved ant colony algorithm reduces the average distance of the distribution path by 1.6%,and the convergence speed is increased by 35.7%.