Research On Dynamic Resource Scheduling In Cloud Manufacturing

With the rapid development of cloud computing,Internet of Things and other IT technologies.The agility,intellectualization,high efficiecy and green has become the theme of manufacturing development.In this context,in 2010,Academician Li Bohu and his team took the lead in proposing a new model for manufacturing-Cloud Manufacturing.After years of development,the cloud manufacturing model has achieved phased results in both theoretical research and practical applications.Resource scheduling is a core component of the cloud manufacturing model.With the rapid development of cloud manufacturing related technologies,efficient and rapid resource scheduling in cloud manufacturing environments has attracted wide attention of researchers.Scheduling is an effective means for cloud manufacturing systems to intelligently complete manufacturing tasks and manufacturing resource matching and as a "brain".Firstly,this parper has studied the related literatures at home and abroad.And then expounded the research background and research significance of this paper.And last the research content of this paper is put forward.Secondly,the types of manufacturing tasks and the characteristics of manufacturing resources in the cloud manufacturing environment were analyzed.In this paper,we research the dynamic scheduling of resources based on manufacturing task changes in the cloud manufacturing environment,and simulate the simultaneous execution of multiple types of tasks in the cloud manufacturing service platform.And we consider various types of emergencies in the scheduling process,such as new task addition,task attribute change and task revocation.At the same time,we also consider the order of execution between the secondary manufacturing tasks and the physical distance between different execution resources.In this paper,a multi-objective optimal scheduling model with minimum total manufacturing service time,minimum total manufacturing service cost,highest average manufacturing service satisfaction and minimum load-to-resource balance is constructed.The weights between the indicators are determined by AHP.What's more,the multi-level coding genetic algorithm is used to solve the problem.The simulation results show that the established scheduling indicators are comprehensive and effective.Based on the scheduling mathematical model established by the scheduling indicators,the main factors affecting resource scheduling in the cloud manufacturing environment can be integrated.At the same time,complex scheduling problems can be transformed into mathematical problem solving.Finally,in this paper,we study the scheduling problem based on the dynamic change of manufacturing resources in the cloud manufacturing environment.Analyzing the dynamic characteristics of resources,and reseach the changes based on resource attributes,such as new resource access,resource maintenance and resource withdrawal.In this paper,a scheduling model with total manufacturing service time,total manufacturing service cost,average manufacturing service efficiency,and average resource reliability as optimization targets is established.The improved particle swarm algorithm is used to solve the model.In this paper,the effective processing of the scheduling system under multi-interference conditions is realized.The flexibility of the scheduling system is enhanced.