计及多目标约束的成品卷烟销售订单配送调度优化  

Optimization of multi-objective constrained distribution scheduling of finished cigarette sales orders

作　　者：欧阳世波[1] 张淼寒 谢俊明 安裕强[1,4] 韩宇航 周心博 潘楠 OUYANG Shibo;ZHANG Miaohan;XIE Junming;AN Yuqiang;HAN Yuhang;ZHOU Xinbo;PAN Nan(Logistics Center Hongyun Honghe Tobacco(Group)Co.,Ltd.,Kunming 650231,China;Faculty of Civil Aviation and Aeronautical,Kunming University of Science&Technology,Kunming 650500,China;Staff Training College,China National Tobacco Corporation,Zhengzhou 450008,China;Faculty of Management and Economics,Kunming University of Science&Technology,Kunming 650500,China;Faculty of Information Engineering and Automation,Kunming University of Science and Technolog,Kunming 650500,China)

机构地区：[1]红云红河烟草(集团)有限责任公司,物流中心,云南省昆明市650231 [2]昆明理工大学,民航与航空学院,云南省昆明市650500 [3]中国烟草总公司,职工进修学院,河南省郑州市450008 [4]昆明理工大学,管理与经济学院,云南省昆明市650500 [5]昆明理工大学,信息工程与自动化学院,云南省昆明市650500

出　　处：《中国烟草学报》2024年第2期80-91,共12页Acta Tabacaria Sinica

基　　金：红云红河烟草(集团)有限责任公司科技项目“计及多维目标优化的成品烟物流调度关键技术研究”(No.HYHH2021XX04);红云红河烟草(集团)有限责任公司科技项目“卷烟成品智慧物流园区建设研究”(No.HYHH2020XX03)。

摘　　要：针对制造业供应链中的跨地域多运输中心大数据订单物流调度问题,对涉及多约束条件和多目标的异构运输车辆调度问题展开研究。根据烟草制造工业的实际运输任务需求,考虑制造订单安排、车辆准运证限制、运输车辆物理限制、运输时间窗、排产出库时间等实际约束,搭建了以包括车辆等待卷烟出库的最短运输时间,最低运输费用及最高车辆利用率为优化目标的成品烟物流车辆跨地域调度优化模型。设计了基于差分进化的混合多元宇宙算法(DE-MVO)用以解决该模型。最后基于某烟草工业企业的实际生产订单数据进行实验,将所设计的算法分别与粒子群(PSO)算法、差分进化(DE)算法、鲸鱼优化(WOA)算法、遗传优化(GA)算法、乌鸦搜索(CSA)算法、天鹰座优化器(AO)、多元宇宙(MVO)等前沿算法进行横向对比,对比结果表明所设计的算法在全局搜索能力、寻优精度等方面更优于其他算法,进一步表明所建立的模型在求解制造业供应链运输车辆调度问题中具有可行性。Aiming at the scheduling problem in the cross-region multi-transportation center logistics scheduling model in the manufacturing supply chain under big data context,the heterogeneous vehicle scheduling problem involving multiple constraints and multiple objectives is investigated.Based on the actual transportation task requirements of the tobacco manufacturing industry,a heterogeneous vehicle scheduling model with the shortest transportation time,lowest transportation cost,and highest vehicle utilization rate as the optimization objectives are built.A hybrid multivariate universe algorithm based on differential evolution is designed.Finally,based on the actual order data of a tobacco industrial enterprise,the designed algorithm is compared with the cutting-edge algorithms such as particle swarm algorithm,differential evolution algorithm,whale optimization algorithm,crow search algorithm,Aquila Optimizer,and multiverse algorithm.The results showed that the designed algorithm outperformed other algorithms in terms of global search capability and convergence rate.It further shows that the developed model is feasible for solving the vehicle scheduling problem of the manufacturing supply chain.

关 键 词：多目标约束 成品卷烟 配送调度 路径优化 多元宇宙算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F252[自动化与计算机技术—控制科学与工程]