Evaluating of dynamic service matching strategy for social manufacturing in cloud environment

As a new form of manufacturing industry in cloud environment, social manufacturing has its inherent “social-cyber” complexity: the source of enterprise services is social, and this sociality aggravates the diversity, uncertainty and dynamics of service supply. Researchers have done a lot to improve the adaptability of service matching strategies. However, it is difficult or even impossible to for traditional methods to evaluate the viability of these strategies because of the “social-cyber” complexity. This poses new challenges to how to evaluate and optimize these strategies in a complex social manufacturing environment. Aiming at the problem, this paper proposes a computational experiment-based evaluation framework, which can simulate all kinds of actual scenarios to verify the performance of service matching strategies. This method includes three parts: (1) design of supply & demand matching strategy; (2) construction of computational experiment system; (3) performance evaluation of service strategies in different experiment environments. A case study is given to verify the applicability of our method by means of comparing several adaptive service matching strategies (supply-oriented, demand-oriented, initial supply & demand-oriented, optimized supply & demand-oriented) in two kinds of market environments. The results demonstrate that our method has a substantial promise.