摘要
随着“双碳”战略的深入推进,绿色低碳物流已成为推动经济高质量发展的重要支撑。针对城市多中转站配送体系中存在的路径冗余、能耗高、碳排放大等问题,文章研究了基于改进遗传算法的多中转站带时间窗低碳车辆路径优化问题。在满足客户需求、车辆容量及时间窗约束的前提下,构建了以综合成本最小化为目标的优化模型,涵盖车辆折旧、燃油消耗、人工成本、时间窗惩罚及碳排放等要素。设计融合自适应交叉、局部变异与种群扩张机制的改进遗传算法,并结合实际数据进行仿真实验。结果表明,通过优化末端网点布局与整合中转站,显著提升了车辆利用率和服务效率,降低了总行驶距离与碳排放量,实现了经济效益与环境效益的协同提升,为城市绿色物流系统的可持续发展提供了理论支撑与实践参考。
With the deepening of the"dual carbon"strategy,green and low-carbon logistics has become a key driver for high-quality economic development.Aiming at the problems of route redundancy,high energy consumption,and excessive carbon emissions in urban multi-depot distribution systems,this paper investigates the multi-depot vehicle routing problem with time windows under a low-carbon objective.An optimization model is established to minimize comprehensive costs,including vehicle depreciation,fuel consumption,labor cost,time window penalty,and carbon emissions,while satisfying customer demands,vehicle capacity,and time window constraints.An improved genetic algorithm incorporating adaptive crossover,local mutation,and population expansion mechanisms is designed to enhance solution efficiency and convergence performance.Simulation experiments based on real logistics data demonstrate that optimizing terminal node layout and depot consolidation significantly improves vehicle utilization and service efficiency,reduces total travel distance and carbon emissions,and achieves coordinated improvements in both economic and environmental benefits.This study provides theoretical support and practical references for the sustainable development of urban green logistics systems.
作者
辜羽洁
GU Yujie(Jiangxi V&T College of Communications,Nanchang 330013,China)
出处
《物流科技》
2026年第7期26-30,37,共6页
Logistics Sci Tech
基金
2024年度江西省教育厅科学技术研究项目“基于改进遗传算法的低碳物流配送路线优化研究”(GJJ2405304)。
关键词
低碳配送
路径优化
改进遗传算法
时间窗约束
low-carbon distribution
path optimization
improved genetic algorithm
time window constraint
