Research And Implementation Of Intelligent Scheduling System For Steel Logistics

The steel industry is a pillar industry of China’s national economic development and an important foundation for the development of the real economy.With the continuous development of new network technologies and applications,the intelligent and digital transformation of the iron and steel industry is gradually entering a critical period.Among them,steel logistics is an important part of the steel industry supply chain.Currently,the traditional steel industry still adopts a manual dispatching method dominated by knowledge workers in the logistics dispatching process,where orders are assigned on site in a queue at the entrance of the plant.This method is inefficient,has low global benefits,and does not meet the requirements of carbon emissions.On the other hand,in logistics order distribution scenarios,especially when transporting multiple orders at once,manual decisions on distribution order based on experience lead to uncontrollable time and costs,and are prone to additional scheduling operations.Due to the long-standing problems mentioned above,there are few systematic intelligent dispatching systems in steel enterprises as a way to optimize the logistics dispatching structure.To this end,this paper designs and proposes an intelligent dispatching system for steel logistics.The system focuses on logistics order allocation,distribution path planning and scheduling management of the unloading and pick-up process,and conducts research based on real application scenarios to realize intelligent and digital logistics scheduling services in line with industry characteristics.To address the problems of poor efficiency and low profitability of manual order allocation,this paper proposes a multi-objective optimization decision model of steel finished goods logistics order allocation based on Markov decision Process.First,a Markov decision model is established as a way to predict the possible revenue situation of drivers when distributing different orders;subsequently,multi-objective constraints are considered,taking into account factors such as drivers’ revenue,enterprise logistics management costs and order constraints,to further assigning weights to the possible matching results between drivers and orders.Finally,the KuhnMunkers algorithm is introduced to perform weighted dichotomous graph matching,with maximizing the total transaction volume of steel commodities and global cost optimization as the optimization objectives,forming a complete matching decision between vehicles and orders of finished steel products.For the lack of intelligent decision making in logistics order distribution planning,a multi-stage steel order distribution path planning decision model based on the improved Dijkstra algorithm is proposed for the steel logistics distribution scenario with one vehicle and multiple orders.Based on the special characteristics of steel logistics distribution and goods,the distribution planning is abstracted as a graph problem,and the vehicle scheduling scheme before entering the plant,the in-plant loading scheduling scheme and the distribution scheme after leaving the plant are also considered.For the optimized model,comparison experiments of shortest path first,cost first and efficiency first are conducted,and the results show that the model can provide a better path planning decision scheme.Based on the above research work,this paper designs and implements the intelligent scheduling system for steel logistics based on the microservice architecture,using the real business data of steel enterprises to train and verify the availability of algorithms.The results show that the two decision models proposed in this paper have certain optimization effects on the original logistics scheduling decision model in the steel logistics scheduling scenario,and effectively solve the problems of steel enterprises in steel logistics order allocation and distribution planning.The paper is tested for the whole system,and the results show the effectiveness of the system in the real scenario of steel logistics.