基于改进蚁群算法的物流配送车辆路径优化方法

目前路径优化方法忽略了客户时间窗约束产生的惩罚成本，导致惩罚成本过高，无法得到最优配送路径，基于此，提出基于改进蚁群算法的物流配送车辆路径优化方法。结合遗传算法完成对蚁群算法的改进，对物流配送车辆路径问题进行建模，得到路径规划问题的目标函数，并根据配送过程的实际情况和具体要求设定目标函数的约定条件，计算固定成本和变动成本为路径优化提供判断依据，设计出路径优化问题的算法流程。在算例分析中，选择某生鲜企业的物流配送作为算例，实验结果表明，设计的方法得到的最优路径总体成本远远低于传统方法，说明所提方法实用性较强。

Vehicle Routing Optimization Method for Logistics Distribution based on Improved Ant Colony Algorithm

At present, the path optimization method ignores the penalty cost generated by the time window constraint of the customer, resulting in the high penalty cost and the inability to obtain the optimal distribution path. Based on this, an optimization method for the logistics distribution vehicle path based on the improved ant colony algorithm is proposed. Complete genetic algorithm combining with improvements on ant colony algorithm, the model of logistics distribution vehicle routing problem is the objective function of the path planning problem, and according to the actual situation of distribution process and the conditions of specific requirements to set the terms of the objective function, to calculate the cost of fixed and variable costs to provide judgment for path optimization, design the arithmetic flow path optimization problem. In the example analysis, the logistics distribution of a fresh enterprise is selected as the example. The experimental results show that the total cost of the optimal path obtained by the design method is much lower than that of the traditional method, indicating that the proposed method is more practical.