Optimization Of Distribution Paths Of DB Logistics Company Based On Ant Colony Genetic Hybrid Algorithm

In recent years,with the continuous progress of China’s economy and information technology,the logistics industry has developed rapidly.Solving logistics distribution path problems through intelligent algorithms is an important means to achieve the core competitiveness of logistics enterprises in the market.Logistics distribution is one of the important links of logistics system,and is an important part of logistics cost,the cost of which directly affects the cost and benefit of logistics.Compared with traditional optimization algorithms,Heuristic Algorithms have the advantages of high efficiency,short time and strong search capability in solving distribution path optimization problems.Ant Colony Algorithm and Genetic Algorithm,as classical heuristic algorithms,are more effective in solving the distribution path problem.Therefore,this paper is mainly based on the Ant Colony Genetic Algorithm to solve the optimization problem of logistics distribution path.This paper takes DB logistics company as the research object and conducts an in-depth study on the problems related to logistics distribution path.By adding the constraints of delivery models and two-way pickup and delivery,and taking the shortest delivery distance as the goal,a multi-model pickup and delivery delivery path optimization model is established,and the model is designed to be solved by Ant Colony Genetic Hybrid Algorithm,and its effectiveness and practical application value are analyzed by simulation experimental results.Firstly,this paper reviews the current situation of related research at home and abroad,and describes the concept,classification,objective,components and distribution path optimization algorithm of distribution path,so as to lay the theoretical foundation for distribution path optimization modeling in the later paper.Secondly,for the two-way logistics distribution path optimization problem,the mathematical model is established with the goal of shortest distribution distance by considering the number of models,vehicle capacity and other constraints.Again,comprehensive analysis of the Ant Colony Algorithm in the early stage of the algorithm due to the low pheromone content and concentration,there are poor search ability,low efficiency,local optimal and other problems,Genetic Algorithm in the early stage of the algorithm is more efficient,global better,and therefore need to introduce genetic algorithm to improve the performance of the algorithm,stage genetic algorithm after coding,genetic and other operations to output a better solution,the better solution as the initial pheromone to guide the ant colony to find the best,to accelerate the algorithm search operation,and defines the maximum number of iterations and the fitness function value as the best integration point between the ant colony algorithm.Finally,MATLAB software is used to set three important parameters,namely pheromone heuristic factor,expectation heuristic factor and pheromone volatility coefficient,to dynamically design the pheromone volatility coefficient,to formulate the pheromone concentration taking rules,to improve the heuristic function and to limit the pheromone update as the key points to improve the Ant Colony Algorithm.The experimental results through MATLAB simulation show that the Ant Colony Genetic Hybrid Algorithm is effective in improving vehicle loading rate,shortening distribution distance,improving transportation efficiency and reducing logistics cost,etc.It accelerates the goal of shortest distribution distance for DB logistics company and provides some ideas for DB logistics company to solve the distribution path problem of larger scale.