Decision field theory-combined multi-attribute group decision-making method for incomplete linear ordinal ranking

Abstract

In practice, experts sometimes compare alternatives from different perspectives by rankings to express their preferences. However, it is common that the comparing information they give is incomplete, and experts’ preferences are changing during the decision-making process. In this situation, it is not easy to depict experts’ preferences and making appropriate decisions. Hence, to handle the problems, this paper proposes a multi-attribute group decision-making (MAGDM) method for incomplete linear ordinal ranking (ILOR) information combined with the decision field theory (DFT) from the perspective of process-oriented decision-making. Firstly, the extended preference map and information energy for ILOR are improved. Based on those, the concept of probabilistic utility set (PUS) and some basic operations are proposed to enhance the computability of ILOR, which can convert incomplete ILORs to PUS and depict the experts’ preferences effectively. Then, the framework and the detailed steps of the DFT-combined MAGDM method are presented, in which the psychological difference for PUS is established. The method helps depict the distance felt by experts and the variability of the decision-making process. Finally, the illustrations are conducted to show the usage and features of the proposed method. The illustration shows the good interpretability and accuracy of the method.