Rresearch O Allocation On Optima N In Cloud Al Decision D Manufac N Method Cturing En Of Resour Nvironmen Rce Nt

Cloud manufacturing is an intelligent manufacturing mode based on service and network.The core is centralized management of remote resources and decentralized services.Supported by the service platform,the cloud manufacturing system gathers technology,equipment and other resources to realize manufacturing with the concept of manufacturing as service.Due to the characteristics of resource diversity,user demand diversity and disturbance changes in the production process of cloud manufacturing,the actual manufacturing becomes more complex.Therefore,this paper studies cloud manufacturing resource matching from the aspects of multi-objective optimization algorithm,decision method and game model.Specific research work in this paper is as follows:(1)Aiming at the problems of trusted service,multi-objective optimization and security scheme evaluation of cloud manufacturing resources,an intelligent optimization and decision method of cloud resource security based on trusted service was proposed.A multi-objective model based on resource reliability,time,cost and quality of service is established.An improved particle swarm optimization algorithm with dynamic inertia weight and velocity perturbation strategy is proposed.The optimal cloud security scheme was selected by VIKOR(VIse Kriterijumski Optimizacioni Racun)evaluation method based on G1-improved entropy weight combination of subjective and objective weighting method.Finally,simulation proves the rationality and effectiveness of the method.(2)The incomplete information between the cloud platform operator and the demander leads to the difficult choice of manufacturing services.Therefore,an intelligent optimization method of cloud manufacturing cluster based on incomplete information game is proposed.Firstly,aiming at the interest competition between the demand side and the cloud platform,a static game model based on incomplete information is established,with the goal of each rationally pursuing the maximization of its own income function.In addition,competition rules between the demand side and the cloud platform are proposed,which are introduced into nature through Harsanyi transformation and converted into a dynamic game under complete information to obtain the Bayesian extended formula.The existence and uniqueness of Bayesian Nash equilibrium are proved.In order to improve the convergence speed and solution quality,a particle swarm optimization algorithm based on Gaussian function and perturbation strategy updating was proposed.Finally,the feasibility and rationality of this method are proved by simulation.(3)Aiming at the problems of cloud manufacturing based on Qo S evaluation and dynamic disturbance resource allocation,a dynamic cloud resource intelligent optimization and decision method based on Qo S was proposed.A Qo S evaluation model is established,and indexes such as time,cost,availability,reliability and satisfaction degree are selected as the basis for cloud service selection.In order to improve the global and local search ability of the algorithm,a multi-objective particle swarm optimization algorithm with nonlinear change of inertia weight and second-order oscillation of velocity is proposed.Furthermore,it uses the weighting method based on AHM(Attribute Hierarchy Model)and improvement CRITIC(Criteria Importance Through Intercriteria Correlation)and VIKOR evaluation method of subjective and objective weighting method are used to make decisions.Finally,the rationality and effectiveness of the above methods are verified by simulation.