A two-stage method for large-scale manufacturing service stable matching under uncertain environments in Cloud Manufacturing

Abstract

With the development of Cloud Manufacturing, the scale of manufacturing platforms has increased. Large-scale platforms increase the difficulty of manufacturing sharing, like time consumption and weak matching. To mitigate these problems, a two-stage method is proposed to support large-scale stable matching under uncertain environments. The first level aims to solve the problem of low efficiency. Considering the time-consuming mutual assessment, cloud models are used to present the preference information of the decision-makers. Meanwhile, to reduce the difficulties of sharing, a density-based method was used to elucidate the non-convex preferences of the customers and manufacturing services. Based on the clustering result, a subgroup-to-subgroup matching method is proposed based on the consensus reaching processes. After that, in the second level, game models among customers, manufacturing services, and platforms are established to realize stable matching. Subsequently, to solve the game models, an enhanced grey wolf algorithm with a parallel-searching mechanism has been designed. Finally, a manufacturing sharing platform is considered an example. Also, the proposed method was compared with methods adopted in previous studies to demonstrate the advantages of the proposed model.