Logistics order distribution is an important part of the sales process of finished steel products,and plays a vital role in the overall experience of the sales process and the virtuous cycle of the whole process.In the actual production process,long-term maintenance of manual order distribution model seems to have a richer accumulation of experience and knowledge,but in reality it has become difficult to adapt to the development requirements of a long time dimension.In order to fully consider the lowest logistics cost of sales in a time cycle as well as the higher revenue of the carrier driver,while ensuring the long-term revenue of the enterprise,the Markov decision model is established by considering multi-objective constraints,and the KM algorithm is introduced to perform bipartite graph matching.Based on the multi-objective optimization for maximizing driver revenue and minimizing enterprise cost,with the long-term ultimate goal of maximizing total steel commodity transaction volume,and combined with the value function and the multi-attribute multi-objective optimization function,a complete matching decision of vehicles and goods is formed.Using real business data of steel enterprises as an example,the data is pre-processed and then suitable features are screened for model training and the correctness and usability of the algorithm is verified.The results show that the model can better address the needs of steel companies in order allocation scenarios compared to traditional order allocation methods.
Order allocationMarkov decisionMulti-objective optimizationBipartite graph matching
NETL
NSTL
万方数据
