基于改进蚁群算法的低碳车辆路径问题研究

全球气候恶化危及人类生存环境，物流运输过程中产生的大量温室气体则是祸源之一。本文考虑带有碳排放约束的车辆路径问题（VRP），以车辆行驶里程最短和碳排放量最小为目标，构建了多目标的VRP非线性规划模型。提出了一种改进的蚁群系统算法对该模型进行求解，算法在更新路径上的蚂蚁信息素时引入了混沌扰动机制，此举能降低算法运行时陷入局部最优解的概率并有效提高算法的适应性。同时，对启发因子、状态转移概率、信息素更新等环节进行了优化设计，提高了最优路径的搜索效率。最后，数值仿真实验证明了该算法的求解表现优于同类研究常用的遗传算法和禁忌搜索算法，具有较强的全局寻优能力。在灵敏性和有效性的保证下，本研究所设计的改进蚁群算法能够较好地处理低碳车辆路径问题（LCVRP）。

Research on Low-carbon Vehicle Routing Problem Based on Modified Ant Colony Algorithm

Global climate deterioration endangers human living environment, and the large amount of greenhouse gases produced in the process of logistics transportation which is flourishing nowadays is one of the main causes of this disaster. Carbon emission control of vehicles in logistics transportation industry can help promote the development of green logistics transportation industry, so that great contributions to environment protection can be made. The key to solve this problem is to achieve the shortest running path(the lowest running cost) of the traditional vehicle routing problem, and to ensure that the carbon emission of vehicles in the running process are effectively reduced at the same time.Aim at offering some theoretical suggestions to the problem discussed above, the vehicle routing problem(VRP) with carbon emission constrain is studied in this paper. Firstly, a multi-objective VRP optimization model is established, which contains the objectives of minimizing vehicle mileage and carbon emission. When calculating the vehicle carbon emission, influence factors such as vehicle type, load, speed, travel distance and even road condition are all taken into consideration comprehensively. This can be a good supplement to the research and design of similar literature. Then an improved ant colony system algorithm is proposed to solve the model. Chaotic disturbance mechanism is introduced to update the ant pheromones on the path, which reduces the probability of falling into local optimal solution while the algorithm is running, and effectively improves the adaptability of the algorithm. At the same time, the heuristic factor, state transition probability and pheromone updating are optimized to improve the search efficiency of optimal path.Finally, the numerical simulation results show that this algorithm outperforms genetic algorithm and tabu search algorithm which are commonly used in the same kind of research, and it has strong global optimization ability. Under the guarantee of sensitivity and effectiveness, the improved ant colony algorithm designed in this studyis another feasible heuristic algorithmfor low-carbon vehicle routing problem, and its performance is comparatively good.