摘要
逆向物流路径优化是降低运营成本与减少碳排放的关键环节,但现有研究在快递包装回收领域仍存在不足,尤其在新能源车辆与智能算法协同优化方面亟待深化.本文针对多回收点场景,构建多约束路径优化模型,以总成本最小化为目标,综合考量固定成本、能耗成本、时间窗惩罚及车辆容量等要素.模型假设新能源货车动态调整行驶速度(与距市区距离成反比),且单位能耗随速度递增.为提升求解效率,提出一种改进蚁群算法,通过邻域搜索策略(路径交换、插入与反转)增强局部寻优能力,结合自适应信息素更新与精英蚂蚁策略,有效平衡全局探索与局部开发.仿真实验表明,相较于传统算法,改进算法在总成本、准时率与计算效率方面均显著优化:运输总距离节省18.7%,固定费用节省20%,变动费用节省18.7%,总成本节省19.57%,并且收敛速度比未改进蚁群算法优化29%.研究为快递包装绿色回收体系提供了低成本、低排放的路径规划方案,对推动逆向物流低碳化发展具有实践意义.
Reverse logistics path optimization is a key link to reduce operating costs and carbon emissions,but the existing research is still insufficient in the field of express packaging recycling,especially in the collaborative optimization of new energy vehicles and intelligent algorithms.In this paper,a multi-constraint path optimization model is constructed for the multi-recovery point scenario,with the goal of minimizing the total cost,and comprehensively considering the fixed cost,energy consumption cost,time window penalty,and vehicle capacity.The model assumes that the NEV vehicle dynamically adjusts its speed(inversely proportional to the distance from the city),and the unit energy consumption increases with the speed.In order to improve the solving efficiency,an improved ant colony algorithm was proposed,which enhanced the local optimization ability through the neighborhood search strategy(path swapping,insertion and reversal),combined with adaptive pheromone update and elite ant strategy,to effectively balance global exploration and local development.Simulation results show that compared with the traditional algorithm,the improved algorithm is significantly optimized in terms of total cost,on-time rate and computing efficiency:total transportation distance is reduced by 18.7%,fixed costs by 20%,variable costs by 18.7%,and total costs by 19.57%.Furthermore,its convergence speed is 29%faster than the basic ACO algorithm.This study provides a low-cost and lowemission path planning scheme for the green recycling system of express packaging,which is of practical significance for promoting the low-carbon development of reverse logistics.
作者
张英彦
李星星
ZHANG Yingyan;LI Xingxing(School of Business,Suzhou University,Suzhou Anhui 234000,China;School of Economics and Managemen,Anhui University of Science and Technology,Huainan Anhui 232001,China)
出处
《太原师范学院学报(自然科学版)》
2025年第3期33-40,共8页
Journal of Taiyuan Normal University(Natural Science Edition)
基金
2021年度安徽省高校人文社会科学研究重大项目(SK2021ZD0092)
宿州学院联合培养研究生科研创新基金项目(2025KYCX15)。
关键词
逆向物流
路径优化
蚁群算法
碳排放
VRP
reverse logistics
path optimization
ant colony algorithm
carbon emissions
VRP
