This paper proposes a large-scale group decision-making model with cooperative behavior based on social network analysis considering propagation of decision-makers’ preference, which is applicable for large-scale group decision-making problems in social network contexts. The main contributions of our research are three aspects. Firstly, a novel calculation method of cooperative degree, hesitant degree, and noncooperative degree is developed, which considers both the network status and the preference for each DM, and thereby it can better represent the current state for each DM. Then, the determination method of each DM’s weight is presented, which considers both the individual network centrality and preference similarity degree. In addition, the score for the current cooperation situation is performed, and the improvement algorithm of the increase of cooperative degree and the decrease of noncooperative degree is designed to enhance the quality of the decision-making results. Finally, the proposed model has demonstrated the validity and superiority based on the comparative and sensitive analysis through a practical example.

This study proposes a large-scale group decision-making (LSGDM) consensus model considering the experts’ adjustment willingness based on the interactive weights’ determination, which can be applied to an LSGDM problem through a case of earthquake shelters. The main contributions of our research are of three aspects as follows. First, the determination method of the interactive weight, which obtains the DMs’ attitude towards the decision-making results, is presented. The subgroups’ weights are calculated, and the unit adjustment cost for each DM is defined. Second, we introduce an objective consensus threshold by the mean and variance of the consensus level for each subgroup. Subsequently, an identification rule is performed to determine the DM to be adjusted with the large difference and the low adjustment cost. And we developed a novel consensus adjustment method that takes the DMs’ adjustment willingness into account to retain as much original information as possible. Thirdly, in order to reduce the subjectivity of the preset consensus threshold and the maximum number of iterations, an objective consensus termination condition that combines the current group consensus level and the consensus adjustment rate is put forward. Finally, the proposed model has demonstrated its effectiveness and superiority based on the comparative and sensitive analysis through a practical example.

Countries around the world advocate low-carbon, green, and environmentally friendly lifestyles to combat climate change, which provides clear direction for enterprise decisions. This paper studies a low-carbon dual-channel supply chain based on behavioral economics, incentive theory, and optimization models to better formulate pricing decisions. This paper constructs a fair and neutral decentralized decision-making model (FNDD), a decentralized decision-making model considering Nash bargaining fairness concerns (NBFDD), a decentralized decision-making model considering absolute fairness concerns (AFDD), and a fair and neutral centralized decision-making model (FNCD) considering consumer preferences and the situations where supply chain members are fairness concerns or fairness neutrality. This paper analyzes the effect of low-carbon advertising level on pricing strategies of online retailers and offline stores and compares pricing strategies of online retailers and offline stores in four decisions. The results show that Nash bargaining fairness concerns of supply chain members could effectively reduce the retail price of low-carbon products and increase their sales volumes. Absolute fairness concerns intensify the dual marginal effect of decentralized decision-making.