The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system

Abstract

Service composition and optimal selection (SCOS) technology composes basic services to satisfy various users’ needs and realize enterprise resource efficient allocation and value maximization. Since sharply increasing scale of the cloud manufacturing (CMfg) resource pool, and the growing sophistication of user requests, will make composed service a sharp increase in the quantity, type, dimension and complexity, which cause a big data environment for CMfg’s application and realization, this paper analyzes the difficulties and solutions of SCOS of big data in the future, especially for optimal selection from large-scale composed service execute paths (CSEP), and proposes two phases SCOS method based on case library. Firstly, cases, similar with users’ request, are searched from case library. Secondly, the cases are used to initial the existing optimization algorithm to solve large-scale optimal selection problem. Moreover, case library structure, user’s service request structure, similarity comparison, and realization process are studied. Compared to the existing optimization algorithm method, the prototype system using case library in large-scale CMfg could obtain a better optimization result.