In the consensus reaching process (CRP) of social network group decision making, the noncooperative behavior exhibited by experts will hinder the achievement of group consensus. This article develops a noncooperative behavior management framework based on trust propagation and dynamic cooperation index under bidirectional feedback context. On the one hand, a trust propagation operator with trust decay is established to enhance the trust relationship between noncooperative experts. On the other hand, the fuzzy preference relations are utilized as preference expression structure, and the mutual reinforcing effect between consensus and trust is explored to achieve the dynamic enhancement of cooperation index, thereby facilitating the transformation of nonscooperative behavior. Specifically, a cooperation index is formulated to identify the noncooperation behavior. Subsequently, a noncooperative behavior transformation method by dynamic cooperation index is investigated. Finally, a bidirectional feedback mechanism is provided for group consensus reaching. This paper provides an innovative strategy for detecting and managing noncooperative behavior, an illustrative example and some analyses are presented to verify the validity of proposed method.

This article proposes a comprehensive star rating approach for cruise ships by the combination of subject and objective evaluation. To do that, it firstly established a index system of star rating for cruise ships. Then, the modified TOPSIS is adopted to tackle objective data for obtaining star ratings for basic cruise indicators and service capabilities of cruise ships. Thus, the concept of distributed linguistic star rating function (DLSRF) is defined to analyze the subjective evaluation from experts and users. Hence, a novel weight calculation method with interactive group decision making is presented to assign the importance of the main indicators. Particularly, in order to enable decision makers to effectively deal with the uncertainty in this star rating process, it adopts the personalized individual semantics (PIS) model. Finally, data of nine cruise ships is collected to obtain their final star rating results and some suggestions for improving cruise service capabilities and star indicators were put forward.