绿色物流配送下的多车型动态车辆路径优化  

Multi-vehicle dynamic vehicle routing optimization in green logistics distribution

作　　者：姜广田[1] 纪皎月 董佳伟 JIANG Guangtian;JI Jiaoyue;DONG Jiawei(School of Economics and Management,Dalian Jiaotong University,Dalian 116028,China;School of Ship Electrical Engineering,Dalian Maritime University,Dalian 116026,China)

机构地区：[1]大连交通大学经济与管理学院,大连116028 [2]大连海事大学船舶电气工程学院,大连116026

出　　处：《系统工程理论与实践》2024年第7期2362-2380,共19页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金(71771043);辽宁省教育厅项目(LJKR0195)。

摘　　要：针对低碳条件下多车型动态车辆路径优化问题,将研究过程分为预优化和动态调整两阶段,以总成本最低为目标函数构建优化模型,采用改进自适应遗传算法(IAGA)对模型进行求解.预优化阶段是在满足店铺需求、退货、车辆油量、工作时间、道路情况等约束下,采用IAGA算法,生成初始的配送方案;在动态调整阶段,综合店铺需求变化、道路状况、临时退货、以及当前配送车辆位置、载货量及油量情况.通过实验分析验证了模型和算法的可行性,有效地降低了总成本,为企业配送策略的制定提供了良好的借鉴.Aiming at the problem of multi-vehicle dynamic routing optimization under low carbon conditions,the research process was divided into two stages:Pre-optimization and dynamic adjustment.The optimization model was constructed with the lowest total cost as the objective function,and the improved adaptive genetic algorithm(IAGA)was used to solve the model.In the pre-optimization stage,IAGA algorithm is used to generate the initial distribution scheme under the constraints of meeting the store demand,returns,vehicle fuel volume,working hours and road conditions.In the dynamic adjustment stage,the changes in store demand,road conditions,temporary returns,as well as the current distribution vehicle location,cargo load and fuel volume are integrated.Through the experimental analysis,the feasibility of the model and algorithm is verified,and the total cost is effectively reduced,which provides a good reference for the formulation of enterprise distribution strategy.

关 键 词：车辆路径 时变需求 动态调整 改进自适应遗传算法 同时取送货 

分 类 号：U116.2[交通运输工程]