基于混合算法的多停靠点物流路径规划仿真

Simulation of Logistics Path Planning for Multi-stop Points Based on Hybrid Algorithms

现有的多停靠点物流路径规划方法未考虑配送中心的位置,无法合理调整停靠点,导致规划不合理,配送效率低的问题。提出一种基于混合算法的多停靠点物流路径规划方法。先对多停靠点物流路劲规划问题展开描述,计算停靠点间的配送用时和停靠时间,构建路径规划数学模型。采用改进蚁群算法求解所选路径的选取概率,物流配送中心选取,结合改进可见度找到配送中心,依据2opt算法原理调整停靠点,利用遗传聚类算法对停靠点类别进行聚类,得到聚类编码的适应度,结合路径规划数学模型,判定出最佳路径。仿真结果表明,所提方法的配送效率最高,规划效果最佳。

The existing logistics route planning method for multi-stopping points ignores the location of distribution centers, so that the stopping points cannot be adjusted reasonably, leading to unreasonable planning and low distribution efficiency. Therefore, a logistics path planning method of multiple stopping points based on hybrid algorithm was proposed. Firstly, we described the problem about logistics planning of multiple stopping points, and calculated the delivery time and stopping time in stop points. Secondly, we constructed the mathematical model of route planning. Thirdly, we used the improved ant colony algorithm to calculate the selection probability, and chose the logistics distribution center. Combined with improved visibility, we found the distribution center. According to 2 opt algorithm, we adjusted the stopping points and used genetic clustering algorithm to cluster the category of stopping points, and thus to acquire the fitness of clustering code. Based on the path planning mathematical model, we determined the best path. Simulation results prove that the proposed method has the highest distribution efficiency and the best planning effect.