Users' Personalized Requirements Oriented Service Configuration Method Research In Cloud Manufacturing

Satisfying users' personalized requirements is the main challenge of modern manufacturing enterprises, and also is their importantly pursuing goal. However, traditional network manufacturing mode which intends to emphasize business collaboration exists limitations in meeting users' personalized requirements. As a burgeoning network manufacturing mode, Cloud Manufacturing, it involves modern information technologies and traditional manufacturing technologies, which has great advantages in satisfying users'personalized requirements, realizing full sharing and optimization allocation aspect between manufacturing resources and services. Focusing on users' personalized requirements, this paper mainly studied the matching and composition optimization problem of manufacturing service in cloud manufacturing mode which has vital theoretical significance and application value.Mainly studied contents are as follows:Cloud manufacturing service allocation framework oriented to users' personalized requirements was constructed. According to users' personalized requirements, the similarity of cloud manufacturing service was adopted to formalize it. AHP, K-means clustering algorithm and Variable Precision Rough Set were introduced to comprehensively estimate it, quantifying users' personalized requirements. Users' personalized requirements was fully reflected by extending cloud manufacturing service similarity to cloud manufacturing service description model.Cloud manufacturing service matching algorithm oriented to users' personalized requirements was designed. Cloud manufacturing service matching algorithm and cloud manufacturing service conserving algorithm oriented to users' personalized requirements based on QoS attributive value's similarity were proposed,which could select and correct candidate cloud manufacturing service set, reduce solution space of composition optimization,improve compositional efficiency , ensuring that cloud manufacturing service which are the closest to what users expect were provided to users while users get access to and utilize cloud manufacturing service efficiently.A composition optimization model was established based on cloud manufacturing service composition optimization process oriented to users personalized requirements and solved by adopting genetic algorithm. On the bases of analyzing cloud manufacturing service composition optimization process, cloud manufacturing service composition optimization model oriented to users' personalized requirements was constructed. Genetic algorithm was improved by using matrix integer coding and block-crossover strategies and it was adopted to solve composition optimization model. The model's rationality and efficiency were examined by changing the fitness value of chromosomes through cloud manufacturing similarity.Cloud manufacturing service allocation prototype system oriented to users' personalized requirements was designed and realized. Prototype system framework oriented to users'personalized requirements was designed and developed by analyzing requirements of the system's function modules and using Visual Studio development platform which supports C#programming language and SQL Server database, which further verified the rationality and effectiveness of models and algorithm from this thesis.