Abstract
In the cloud manufacturing systems, both manufacturing tasks and manufacturing services are in a dynamic environment. How could cloud manufacturing platform optimizes manufacturing cloud services based on QoS, matching an optimal service composition for manufacturing tasks has become an urgent problem at present. In view of this problem, we study the matching of manufacturing tasks and manufacturing services from the perspective of complex network theory. On the basis of manufacturing task network and manufacturing service network, a dynamic matching network theory model of manufacturing task-service is constructed. And then, we take a dynamic assessment of QoS. Finally, we use load and dynamic QoS as the optimization objectivities, transform the optimal manufacturing service composition problem into the shortest path problem, and the dynamic scheduling of manufacturing services is realized.
Highlights
Because of the diversification of customer needs, the manufacturing industry is increasingly trying to integrate products and service components together to quickly respond to customer needs, shorten product development cycles and maintain agility by creating more complete solutions
Because of the dynamic nature of manufacturing resources, manufacturing services are in a dynamic environment, including the addition of new services, the evolution or extinction of old services
In service-oriented manufacturing systems, the supply is the set of various social available manufacturing resources and capabilities in the form of services.Nodes load of service manufacturing, which are denoted as L, referring to the number of manufacturing tasks currently processed by manufacturing services
Summary
Because of the diversification of customer needs, the manufacturing industry is increasingly trying to integrate products and service components together to quickly respond to customer needs, shorten product development cycles and maintain agility by creating more complete solutions. [1] manufacturing tasks and manufacturing services are in a dynamic and changing environment. Building an integer programming model to match the supply and demand of manufacturing resource services to ensure the optimality of QoS. The existing researches on supply and demand matching and optimal allocation of manufacturing resources (for example, the problem of matching for simple demand or primitive task[7] and matching for complex demand or compound task[8]) are still far from the service-oriented practice. The dynamic changes of manufacturing tasks and manufacturing services pose challenges to the management of cloud manufacturing systems. There is a lack of in-depth research on the supply-demand dynamic matching method in the cloud manufacturing environment, and no mature scheme has been formed. The model will provide a theoretical basis for the dynamic management of manufacturing tasks and manufacturing services
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