Research On Multi-objective Vehicle Path Optimization Based On Improved Ant Colony Algorithm

With the rapid development of social economy,the distribution demand of logistics industry is gradually increasing,but the carbon dioxide produced in the distribution process is an important cause of air pollution.With the proposal of the concept of green economy,how to effectively optimize the distribution path to reduce energy consumption and carbon emissions in the distribution process while ensuring the rapid development of the logistics industry has gradually become a new research direction of Vehicle Routing Problem(VRP).Based on the vehicle routing problem with soft time window and capacity limitation,this paper aims to reduce the total distribution cost and carbon emission in the process of logistics distribution.Multi-objective Vehicle Routing Problem(MO-VRP)under static environment and dynamic environment is studied respectively.The research contents are as follows:(1)On the basis of the existing research results,the VRP problem is elaborated and classified in detail,and the classical vehicle routing problem model and the vehicle routing problem model with time window are introduced,and then the common exact algorithm,heuristic algorithm and meta-heuristic algorithm for solving VRP are analyzed and studied.It lays a theoretical foundation for the modeling and algorithm design of multi-objective in static and dynamic environments.(2)In view of the actual background of energy conservation and emission reduction of logistics enterprises,a multi-objective vehicle routing optimization model with the lowest total distribution cost and the lowest carbon emission is established by comprehensively considering vehicle load,driving speed and distribution distance,etc.At the same time,in view of the shortcomings of the classical ant colony algorithm in solving this problem,the initial pheromone and path transfer rules are redesigned to enhance the global detection and search ability of the algorithm.Secondly,2-OPT algorithm is used to improve the local information detection and search in the network,so as to further improve the quality of understanding.Finally,a new pheromone updating formula and chaotic perturbation mechanism are introduced to update the pheromones along the path to enhance the diversity of understanding.The simulation results show that the multi-objective model can better give consideration to the total distribution cost and carbon emissions in logistics distribution,and provide effective support for decision makers.The proposed improved ant colony algorithm also has faster convergence speed and higher solving accuracy.(3)With the static optimization model of path optimization was idealized environment,has certain limitations,therefore,consider the dynamic nature of road traffic network,on the basis of probability function and road,building dynamic speed function,establish a model of multi-objective vehicle routing based on velocity function,and with the help of the improved ant colony algorithm to solve the model.Through the construction of a variety of dynamic environment simulation tests,the impact of dynamic environment on carbon emissions and total distribution cost is analyzed.After comparing with the classical ant colony algorithm,the effectiveness of the improved ant colony algorithm for the optimization of multi-objective vehicle routing problem in dynamic environment is verified,which provides a reference for the vehicle routing optimization decision in dynamic environment.