基于改进蚁群算法的低碳车辆路径优化

Low-carbon Vehicle Path Optimization Based on Improved Ant Colony Algorithm

为降低物流配送成本,提高配送效率,优化物流车辆配送路径,从节能减排角度出发,综合考虑配送时间窗因素,以运输成本、碳排放成本、时间窗惩罚成本三者之和最小为目标,构建带时间窗约束条件的低碳车辆配送路径优化模型。将基本蚁群算法的信息素更新策略改为局部更新与全局更新相结合的方式、再将其与A*算法、2-opt算法相结合,设计出改进蚁群算法,用Solomon的测试数据进行仿真实验,并将改进蚁群算法与基本蚁群算法进行结果对比。仿真实验结果表明,相较于基本蚁群算法,改进蚁群算法在算法收敛速度更快、使用配送车辆更少的情况下,配送总成本降低了11.25%,配送效率提高了11.91%,说明改进蚁群算法在配送路径优化问题上是有效且可靠的。

In order to reduce the cost and improve the efficiency of logistics distribution,and optimize the path of logistics vehicles.Both consider the perspective of energy conservation and emission reduction and the factors of distribution time window.With the goal of minimizing the sum of transportation cost,carbon emission cost,and time window penalty cost,an optimization model,with time window constraints,of low-carbon vehicle distribution path was constructed.The pheromone updating strategy of the basic ant colony algorithm is changed to a combination of local and global updating,then combined with the A*algorithm and 2-opt algorithm to design the improving ant colony algorithm.Simulation experiment was conducted with Solomon's test data.The results were compared with the improving ant colony algorithm and the basic ant colony algorithm.The results of the experiment show that the improving ant colony algorithm reduces the total cost of distribution by 11.25%and improves the efficiency by 11.91%,when it converges faster and uses fewer vehicles.It shows that the improving ant colony algorithm is effective and reliable in the distribution path optimization problem.