Research On Multi-vehicle Nested Dispatching Planning Of Cold Rolled Intermediate Warehouse In Steel Plant

With the continuous integration of artificial intelligence and logistics technology,the transformation of steel logistics to intellectualization has become inevitable.At present,China’s logistics intellectualization is relatively backward compared with developed countries in Europe and the United States.More than 50%of enterprises in Europe and the United States use intellectualized logistics,but less than 20%in China.Especially,there is still a big gap in automation,intellectualization and networking of steel logistics equipment compared with developed countries.Intelligent logistics,networked logistics,Internet of Things+steel logistics are also proposed in the outline of Made in China 2025.This paper focuses on the research on manual intelligence of workshop-level logistics in iron and steel production.Aiming at the low output efficiency of product flow in the middle reservoir area of cold rolling and finishing work area in domestic iron and steel enterprises,and carries out research on the intelligent nested multi-car system with rail on the ground.The scheduling and algorithm,objective model function,path planning and conflict decision of intermediate warehouse conveying system in cold rolling workshop of metallurgical iron and steel enterprise are analyzed and studied.Finally,validity of the model and algorithm for improving the operational efficiency of the intermediate warehouse logistics transportation system are verified by solving examples.The main research results of this paper are as follows:A configuration decision model for multi-subordinate parent car access system is presented.According to the task scenario mode of the system,the access task is taken as the object,the sub-car as the secondary server,and the parent car as the primary server.The corresponding queuing network mathematical model SCQN(Semi-open Compositive Queue Network)is established,and the optimal demand analysis objective function is designed to solve and check the queuing network model of the system.The results show that the sub-car and the parent car of the system operate in different modes and number configurations.The maximum system throughput and optimal number of sub-mothers are obtained,which provides decision-making theoretical basis for optimizing the design of the whole logistics system,improving the utilization rate of equipment and saving the cost of equipment.A scheduling mathematical model MHFS(Multi-object hybrid flowshop scheduling)based on the iterative principle of flexible flow shop is established.According to the characteristics of multi sub car and multi master car cross compound configuration in the intermediate warehouse logistics transportation system,the situation of the joint operation of storing/taking steel coil and simultaneously executing the steel coil storage and retrieval is analyzed,and the system operation rules and job path expression are formulated,and the system scheduling optimization mathematical model based on the flow shop processing iterative principle is established,and the quantitative analysis of the congestion delay in the operation of the logistics dispatching system is carried out,and the scheduling algorithm optimization under the guidance of formulating the congestion avoidance principle of the system scheduling is proposed to realize the minimum congestion delay of the system scheduling,which opens up a new idea for the research of the whole system dispatching model.According to the demand characteristics of the scheduling model,an improved genetic algorithm GAFS(Genetic Algorithm with Function and Self-learning)is proposed to solve the model.The multi-stage scheduling information coding method of multi-stage sub parent vehicle scheduling information is designed,and the improved population with knowledge self-learning and the expert base genetic algorithm to optimize the compromise solution function are designed.The self-learning is used to update the compromise solution of the expert database to ensure the effectiveness of iterative evolution.Finally,compared with traditional genetic algorithm and particle swarm optimization algorithm,the effectiveness of self-learning knowledge expert base genetic algorithm for solving this kind of flexible logistics scheduling optimization problem studied in this paper is proved.Aiming at the characteristics of multi-vehicle multi-objective route optimization and access volume constraints in the logistics transportation system of intermediate warehouse and the limitations of traditional Dijkstra algorithm,the labeling method of D*algorithm is improved,and an improved algorithm with time penalty factor is presented to solve the optimal routes in each stage of the logistics sub-car system.An improved banker algorithm is used to detect active conflicts on multi-vehicle operation paths on tracks,a time window adjustment strategy for dynamic priority transfer is formulated to eliminate collision conflicts on multi-vehicle common tracks,and system conflict detection and collision avoidance model is established,which effectively reduces the collision probability of the system.The research of steel coil logistics transportation system in the intermediate warehouse of steel plant has filled the gap of logistics technology in the cold rolling intermediate warehouse of domestic metallurgical manufacturing.The relevant research results can provide theoretical basis and technical reference for safe,efficient and intelligent scheduling planning of heavy production logistics in the future.