摘要
研究车辆路径优化问题,物流配送不仅要求配送及时,而且要求运输成本低,且路径最优。车辆路径优化是解决物流配送效率的关键,传统优化方法寻优效率低,耗时长,难以得到车辆路径最优解,导致物流配送成本过高。为了提高车辆路径寻优效率,降低物流配送成本,提出一种混合算法的车辆路径优化方法。首先建立车辆路径优化数学模型,然后用遗传算法快速找到问题可行解,再将可行解转换成蚁群算法的初始信息素,最后采用蚁群算法从可行解中找到最优车辆路径。仿真结果表明,混合方法提高车辆路径寻优效率,有效地降低物流配送成本。
Research vehicle routing problem. Logistics distribution requires the timely delivery and low transportation cost, and the vehicle routing is the key to solve the logistics distribution problem. The traditional optimization method has. the defects of low searching efficiency, time-consuming and high cost. To improve the vehicle route optimization efficiency and reduce logistics cost, this paper proposed a hybrid algorithm for vehicle routing optimization method. Firstly, vehicle routing mathematical model was established, and then genetic algorithm was used to find a feasible solution quickly. Then the solution was converted into the initial pheromone of ant colony algorithm. Finally, ant colony algorithm was used to find the optimal solution from the feasible path. The simulation results show that, compared with other optimization methods, the proposed method can improve vehicle routing optimization and reduce the cost of logistics distribution effectively.
出处
《计算机仿真》
CSCD
北大核心
2012年第5期356-359,共4页
Computer Simulation
关键词
车辆路径问题
蚁群算法
优化
Vehicle routing problem （VRP）
Ant colony algorithm
Optimization
