生产加工与纯电动货车配送的协同优化研究  

Research on Collaborative Optimization of Production Processing and Pure Electric Truck Delivery

作　　者：张明伟 张大鹏 李波[2] ZHANG Mingwei;ZHANG Dapeng;LI Bo(School of Economics and Management,Tianjin Renai College,Tianjin 301636,China;College of Management and Economics,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津仁爱学院经济与管理学院,天津301636 [2]天津大学管理与经济学部,天津300072

出　　处：《计算机工程与应用》2025年第8期363-372,共10页Computer Engineering and Applications

基　　金：国家自然科学基金面上项目(71972142);天津市教委科研计划项目(2019KJ154)。

摘　　要：针对时变网络环境下,城市配送中如何减少纯电动货车的电耗问题,提出将供应链中的生产与运输配送环节协同优化的绿色生产配送调度模型,模型以速度变化为关键变量,电耗为优化目标之一,同时考虑了车辆动态负载、服务时间以及客户需求时间窗口等约束条件。改进了蚁群遗传算法,在初始种群中增加了启发式规则学习;使用Metropolis抽样准则提高算法跳出局部最优的能力;引入电耗因子、完成时间因子、路径长度因子,提高算法进化的方向性。模拟算例表明,模型能够有效减少供应链配送过程中的车辆电耗,同时验证了算法的高效性。Aiming at the problem of how to reduce the power consumption of pure electric trucks in urban distribution under the time-varying network environment,a green production and distribution scheduling model is proposed to optimize the production and distribution links in the supply chain,with speed change as the key variable and power consumption as one of the optimization objectives.The model takes speed change as the key variable,electricity consumption as one of the optimization objectives,and considers constraints such as vehicle dynamic load,service time,and customer demand time window.The ant colony genetic algorithm has been improved by adding heuristic rule learning to the initial population;the Metropolis sampling criterion is used to enhance the algorithm’s ability to jump out of local optima;power consumption factors,completion time factors,and path length factors are introduced to improve the directionality of the algorithm’s evolution.Simulation examples show that the model can effectively reduce vehicle electricity consumption during the distribution process in the supply chain,and the effectiveness of the algorithm is also verified.

关 键 词：绿色供应链 纯电动货车 时变网络 生产配送协同 改进蚁群遗传算法 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] F252[自动化与计算机技术—计算机科学与技术]