Cloud Manufacturing Service Composition Optimization Based On Qo S-aware In Uncertain Environment

With the dynamic changes of the manufacturing environment and the diversification of customer needs,most companies find it difficult to use their limited resources and capabilities to meet complex needs.In this case,more and more manufacturing companies have integrated their advantages,cooperated with each other and shared manufacturing resources(including equipment resources,design resources,data resources and computing resources).Various manufacturing resources and functions are encapsulated as manufacturing services,and then registered and published on the cloud service platform by using service-oriented architecture and Web service technology.And allow customers to choose manufacturing services to enhance their competitiveness.As a result,the problem of optimal allocation of manufacturing resources appears in the manufacturing environment.Therefore,how to respond quickly and select suitable manufacturing services from a large number of candidate services to form a cloud manufacturing service composition has become a major challenge for more and more enterprises.The cloud manufacturing service composition optimization based on Qo Saware has attracted much attention because it directly reflects the overall quality of the composite service.However,most of the existing researches use precise values to represent Qo S attributes without considering the uncertainty of Qo S attributes caused by the uncertain environment in the actual manufacturing process.Aiming at this problem,this paper studies two Qo S-aware cloud manufacturing service composition optimization method under uncertain environments and use two methods to describe the uncertainty.Details are as follow:Firstly,the uncertainty of Qo S attributes is described in two ways.Several Qo S attributes that users and cloud platforms pay more attention to are selected as the indicators to evaluate the service.Triangular fuzzy numbers and interval numbers are adopted to describe the uncertainty of Qo S attributes.The comparison rules,normalization method of using triangular fuzzy number to express Qo S attribute value and ranking rules and constraint processing method of using interval number to express Qo S attribute value are given.At the same time,the aggregation formulas of various Qo S attributes under different structures are given.Then,the optimization method of cloud manufacturing service composition based on triangular fuzzy number is studied.When the user preferences are known,the weights of different Qo S attributes are assigned,and a mathematical model based on triangular fuzzy number is constructed to evaluate the comprehensive Qo S value of service composition.By improving the quality of initial population,introducing dynamic constraint violation degree and local search method,the global and local search ability of the algorithm is improved.Compared with the other two algorithms,this method can solve the cloud manufacturing service composition optimization problem more effectively,especially in the large-scale service composition optimization problem.Finally,the optimization method of cloud manufacturing service composition based on interval number is studied.The interval number is used to express the Qo S attribute value,and the interval multi-objective optimization model considering the connection relationship between adjacent subtasks is constructed.The interval possibility ranking method is combined with non-dominated sorting,and the multiobjective algorithm is used to solve the problem directly.At the same time,the fruitfly optimization algorithm is introduced to improve the local search ability.Through numerical examples,compared with the two multi-objective optimization algorithms,the results show that the algorithm can solve the cloud manufacturing service composition optimization problem considering the connection relationship between subtasks more effectively.