Research On Optimization Algorithm Of Logistics Distribution Routing

In today’s era of rapid development of the logistics industry,the problem of logistics distribution routes has become a difficult problem.The limitation of delivery vehicles and the difference in the time customers receive services restrict the quality and timeliness of logistics delivery services.Therefore,optimizing the logistics distribution path can directly improve the service quality of the distribution personnel,reduce the distribution cost,and then improve the economic efficiency of the enterprise.For this reason,based on the study of the capacity-constrained soft time window logistics distribution route problem,this thesis establishes a mathematical model and optimizes the ant colony algorithm,and then uses the optimized ant colony algorithm to solve the logistics distribution problem with soft time window.The main work of this thesis is as follows:(1)Through analysis and research,a mathematical model for the optimization of the logistics distribution path with capacity constraints and time window constraints is established.The model takes the lowest cost as the objective function,which includes the normal use cost of the vehicle,overtime and deviation from the customer’s expected time window And the penalty cost incurred;(2)In-depth study of ant colony algorithm,and proposed an optimization plan on Max-Min Ant System,re-constructed heuristic function,and added a local update pheromone strategy,so that ant colony algorithm can improve the solution speed and global search ability,and then introduce the crossover and mutation operations of genetic algorithm To update the optimal path to optimize the solution quality of the algorithm.In order to verify the effectiveness of the model and the optimization algorithm,experimental solutions were carried out through the client scales of 25,50,and 100 examples,and the MMAS was used as the control group.The experimental results were the minimum number of iterations for the experimental group to obtain the optimal solution It is 15,65,39,and the control group is 64,85,75.Comparing the results,it is obvious that the optimized ant colony algorithm can converge earlier,and the optimal solution quality is better than that obtained by the ant colony algorithm.This result verifies the rationality of the model and the effectiveness and stability of the optimization algorithm.