Optimization of multi-objective constrained distribution scheduling of finished cigarette sales orders
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.
万方数据
维普
