Research On The Low-carbon Carbon Logistics Routing Optimization Based On Ant Colony Algorithm

With the problem of global warming, carbon problem have become the hot topics of government and academics in the world. Logistics industry costs lots of energy and CO2, so it is necessary to reduce energy consumption and carbon emissions for the development of the logistics industry. Then, it is the reason why low-carbon carbon logistics be produced. Low-carbon carbon logistics aim at low energy consumption, low pollution and low-emission, which used of energy efficient technologies, renewable energy technologies and smart optimization methods to maximize resource utilization and the lowest carbon emissions. For low-carbon carbon logistics, rational planning path and reducing load is effective ways to improve logistics efficiency and reduce carbon emissions. Therefore, optimizing carbon logistics distribution routing mathematical model with modern intelligent optimization method and finding the best delivery route had important theoretical and practical value.The main work is done as follows:(1) On the basis of analyzing the dynamic optimization of low carbon logistics path, discussed the basic theory of low-carbon carbon logistics and ant colony algorithm. And in order to minimize the carbon emissions cost, established low-carbon carbon logistics routing optimization model.(2) Because of the seeking to local optimal solution rather than the global optimal solution and convergence lag of ant colony algorithm, this paper assimilate the crossover and mutation idea of DNA algorithm into ant colony algorithm, to controls the parameter selection, which will improve the convergence rate and search the global optimal solution. At the same time, we also use the DNA-ant colony algorithm to solve the low-carbon carbon logistics routing optimization model. By the simulation, verified the improved ant colony optimization is superior to the basic ant colony optimization.(3) To avoid stagnation and premature phenomenon, the simulated annealing ant colony algorithm with chaotic disturbance was proposed to solve the low carbon logistics routing optimization model. The chaotic system and the simulated annealing method were introduced to the ant colony algorithm, which increased the global searching ability and improved the solving efficiency. Simulation and comparison result shows that the simulated annealing ant colony algorithm with chaotic disturbance get a more satisfactory optimization result compared with ant colony algorithm.(4) This paper use Beijing-Tianjin-Hebei region(BTJR) as the simulation example, we will bring carbon emissions cost to establish the path optimization model based on the optimization objectives of the lowest cost of carbon emissions. Then we use basic ant colony optimization, DNA-ant colony optimization and the simulated annealing ant colony algorithm with chaotic disturbance to simulate model, and given the distribution path of the three algorithms. The results show that the simulated annealing ant colony algorithm with chaotic disturbance find the best result, the second is DNA-ant colony optimization, the last one is basic ant colony optimization.