Research On Manufacturing Resource Service Composition And Optimization In Cloud Manufacturing Environment

With the rapid development and wide application of modern information technologies,the traditional manufacturing enterprises have undergone profound and constant changes constantly.Under this background,cloud manufacturing was put out in recent years.Cloud manufacturing has gradually broken through the limitation of the sharing degree and utilization of resources in the manufacturing industry by integrating distributed manufacturing resources,realizing the idea of centralized management and optimal configuration,thereby promotes the comprehensive sharing of resources and improves utilization of resource.Moreover,enterprise users pay more attention to develop their core competitiveness due to the division of labor becomes more and more refined and specialized in the manufacturing industry,which makes the manufacturing tasks are often completed through a composition of resource services in the cloud manufacturing environment.However,there are a large number of cloud services share similar or even overlapped functionalities but different service qualities in the cloud platform,and how to quickly select the best service composition to meet the interest needs of the three parties from the mass composition solutions and successfully execute the task,has become a key issue for manufacturing resource service composition and optimization.Based on the above analysis,this paper proposes a new method of manufacturing resource service composition and optimization in current cloud manufacturing environment.First of all,the paper studies the manufacturing task decomposition.Considering the impact of the resource pool condition during the task decomposition process,an improved manufacturing task decomposition method is proposed.And then,incorporating the task decomposition into the process of service composition,which aims to avoid the disconnection between task decomposition and resource allocation.Secondly,the paper studies the service composition model and its solution method.And it comprehensively considers the interest needs of the three parties,proposes the control method of the constraint information about quality of task,introduces the confidence interval on the top of task quality control,conducts the service composition conflict prevention,and establishes mathematical model.Besides,an improved fast non-dominated sorting genetic algorithm with elite strategy is proposed.Moreover the Pareto optimal solution set of the service composition is obtained by solving the model,and the effectiveness and efficiency of the algorithm are verified.Thirdly,the paper studies the service composition optimization and the service composition conflict resolution,and proposes an impoved grey relation analysis method and a service composition conflict resolution method.For one thing,the Pareto optimal solution set of the service composition is evaluated and optimized,which provides the optimal service composition and alternative solution set for the decision makers.For another,this paper analyzes the different kinds of service composition conflicts,proposes corresponding conflict resolutions to dynamically push the preferred service composition for the user from a practical perspective,and achieves dynamic optimization of service composition.Finally,the study results of this paper are applied in practice,and the process is illustrated.Then the rationality and effectiveness of the proposed method are proved by the application results.