基于改进蚁群算法的电动冷藏车配送路径优化研究  

Research on Distribution Route Optimization for Electric Refrigerated Vehicles Based on an Improved Ant Colony Algorithm

作　　者：瞿丹 戚磊 王星 QU Dan;QI Lei;WANG Xing(School of Management,North Sichuan Medical College,Nanchong 637000,China;School of Basic Medicine and Forensic Medicine,North Sichuan Medical College,Nanchong 637000,China;School of Pharmacy,North Sichuan Medical College,Nanchong 637000,China)

机构地区：[1]川北医学院管理学院,四川南充637000 [2]川北医学院基础医学与法医学院,四川637000 [3]川北医学院药学院,南充637000

出　　处：《郑州航空工业管理学院学报》2025年第2期104-112,共9页Journal of Zhengzhou University of Aeronautics

基　　金：四川省自然科学青年基金项目(2023NSFCS1676);南充市社会科学研究“十四五”规划2024年度项目(NC24C140);川北健康人文研究中心项目(NC25CB56)。

摘　　要：电动冷藏车配送产品具有较强的时效性,需要对其行驶路径进行科学高效地规划。以总成本最低为目标构建路径规划数学模型,设计一种改进蚁群算法求解。传统蚁群算法通常仅考虑客户点间距离和信息素量对转移概率的影响,未考虑客户点的服务时间跨度。而在路径上,信息素量过多会陷入局部最优,或者信息素量过低会使算法收敛性降低,并且对所有节点搜索到的所有信息素进行更新导致算法冗余。针对这些缺陷,通过插入时间因子改进路径转移规则、设定信息素浓度临界范围、优化信息素更新策略来明确算法实现步骤,并采用Solomon算例测试改进算法的有效性和可行性。结果表明:改进蚁群算法适用于小规模、大规模随机分布、聚类分布和随机聚类分布场景下的配送情境,并且客户分散程度与总成本呈正相关。相比传统蚁群算法,改进后的寻优时间缩短约50%,寻优目标值减小4.2%,行驶路径缩短4.5%。这说明改进后的蚁群算法对解决传统算法收敛速度慢、容易陷入局部最优等问题显著有效。Electric refrigerated vehicles require scientific and efficient route planning for the timely delivery of perishable goods.To build a mathematical model for route planning with the lowest total cost,an improved ant colony algorithm is designed.Traditional ant colony algorithms primarily consider the influence of distances between customer points and pheromone quantities on the transfer probability,but neglect the service time span of customer points.This may lead to the local optimum if too much pheromone is used on the path,or cause the decrease in the algorithm's convergence rate if too little pheromone is used.Additionally,the algorithm becomes redundant when updating all the pheromone on all nodes.To address these issues,improvements are proposed by incorporating a time factor into the transition rule,setting a pheromone concentration range,and optimizing the pheromone update strategy,so as to elucidate the algorithm implementation steps.Solomon Benchmark Instances are used to test the effectiveness and feasibility of the improved algorithm.The results show that the improved ant colony algorithm is suitable for delivery scenarios with small-scale,large-scale random,clustered,and random-clustered distributions,and that customer dispersal degree is positively correlated with total cost.Compared with the traditional ant colony algorithm,the improved algorithm reduces search time by about 50%,lowers optimization objective value by 4.2%,and shortens the travel distance by 4.5%.These findings indicate that the improved ant colony algorithm is significantly effective in enhancing convergence speed and avoiding local optimum.

关 键 词：电动冷藏车 配送路径优化 改进蚁群算法 

分 类 号：U121[交通运输工程]