Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources

Abstract

Owing to the complexity of socio-economic environments and the fuzziness of human cognition, information of cognitive preference provided by decision-making organizations composed of many experts is often hesitant and fuzzy. In consequence, for the sake of addressing the hesitance and fuzziness of preference information for the configuration of tasks and resources in cloud manufacturing, a decision-making model of a two-sided matching considering a bidirectional projection under preference information of hesitant fuzzy is put forward. Primarily, this paper describes the problem of two-sided matching and introduces the hesitant fuzzy set. Afterwards, according to preference information given by matching agents using the hesitant fuzzy element, the evaluation matrix is constructed. Meanwhile, the bidirectional projection technology and TOPSIS method are combined to calculate the closeness degree matrix. Further, by introducing the constraint of the stable matching, a decision-making model of a two-sided matching for maximizing the closeness degree of two-sided matching agents is constructed, and the optimal configuration results are obtained by the solving of the model. Subsequently, the illustrative case is provided to validate the rationality and effectiveness of the presented model in solving the configuration for cloud manufacturing tasks and resources. Also, the stability in the proposed configuration results is illustrated by making a sensitivity analysis. Further, the reliability in the given solving process is demonstrated through performing a comparative analysis, as well as the advantages in the proposed model is also discussed. It shows that this developed model can give stable configuration results and also provide a matching approach for different agents under an uncertain environment.