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

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.