Knowledge structure-based consensus-reaching method for large-scale multiattribute group decision-making

Abstract Large-scale multiattribute group decision-making (LMGDM) requires a large number of participants with different knowledge structures. This study proposed an LMGDM consensus-reaching method in which the experts’ knowledge structures are fully considered. An information extraction mechanism is constructed to extract incomplete inference information with the form of belief distribution (BD), and the Dempster–Shafer theory of evidence is adopted to make discounting and combinations for the BDs. To reduce their number, the experts are classified into different clusters by using the extended K-means approach, and two levels of consensus measures are both calculated to determine whether the experts involved in each cluster have reached a satisfactory level of consensus. If that consensus level is not reached, a feedback mechanism is activated to advise the identified experts to adjust their assessments, which allows them to change clusters during the consensus-reaching process. Through repeating the feedback mechanism, the assessments are improved until the satisfactory consensus levels are reached. A multi-objective linear programming method is established to obtain the optimal solution that satisfies all clusters as much as possible. Finally, a numerical comparison and discussion are undertaken to demonstrate the superiority of the proposed method.