Stochastic-simulation-based multi-attribute group decision-making method under uncertain environment and the application

For multi-attribute group decision-making with interval uncertainties, this paper proposes a stochastic-simulation-based multi-attribute group decision-making model that considers the relative superiority or inferiority between any two alternatives for a comprehensive evaluation. First, a novel attribute weights method is proposed by reducing the decision uncertainty based on the stochastic simulation method, where the uncertainty is determined by the superiority or inferiority of pairwise comparisons of alternatives on the associated attribute. Based on this, the attribute weights are calculated using a programming model that aims to minimise the uncertainty between the alternatives and the ideal state. The uncertain attribute values provided by each expert are aggregated into individual possibility rankings. Subsequently, a novel information aggregation method that considers group consensus is introduced, in which the consensus level among all experts is measured based on individual possibility rankings. The consensus measurement of individual possibility rankings allows to identify high- and low-consensus decision-making information within expert judgements. This process facilitates recognition of the consensus quality of each expert’s decision-making information. The final possibility-ranking is then aggregated based on the consensus quality of the various possibility decision-making information. Finally, this study uses a numerical example of the evaluation of a company's performance to demonstrate the effectiveness and application of the proposed method.