For the supplier selection of manufacturing enterprise in cloud manufacturing environment, a larger range of choices and the wide distribution of manufacturing resources are highly shared compared with the traditional manufacturing environment. Moreover, the fuzzy features of QoS bring new challenges for the supplier selection in cloud manufacturing environment. Therefore, large quantity of resource, semantic information asymmetry, and fuzzy of QoS become the key problems in supplier service matching.
On a cloud manufacturing platform, the suppliers as services can be described by functional information and QoS information. Functional information is composed of concepts, numerical and interval. QoS information is represented by fuzzy language. Because of the large number of suppliers, functional information of supplier s1 and supplier s2 are probably the same, but they almost have different QoS information. Accurate matching results can be obtained by matching the two kinds of information in two services.
In this paper, a three-phase service matching model is proposed based on ontology and fuzzy QoS clustering. Firstly, a description models of service description and ontology is established with semantic ontology in order to eliminate the asymmetry of information and increase the integrity of the semantic information. Secondly, the multiple attributes of QoS based on the triangular fuzzy number are established by combine with fuzzy preference and optimize fuzzy c-means clustering algorithm (FCM), greatly improve the speed and efficiency of convergence. Finally, the experiment is conducted according to real automobile supplier data and expert opinions, and the results from the actual experiment have shown that this method can achieve higher precision and adaptability compared with the traditional methods. In this study, new idea,whinch is about how to solve the problem of service matching in cloud manufacturing environment is put forward.
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